Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/SMC53992.2023.10394655

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Other Link： https://dblp.uni-trier.de/db/conf/smc/smc2023.html#KobayashiTMMMFT23

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

DOI： 10.1007/s10015-022-00826-y

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Other Link： https://link.springer.com/article/10.1007/s10015-022-00826-y/fulltext.html

Language：English   Publishing type：Research paper (international conference proceedings)  

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Multi-legged locomotion is generated from multilevel control integration, from lower to higher. It is still become challenging for a robotic developer. This research builds the loco-motion model that integrates embodiment, perception, cognition, and knowledge building. The proposed model considers internal sensory information and external sensory information. It involves a multi-level control to solve the complexity of multi-modal system integration, a neuro-science and ecological psychology approach to developing the proposed system architecture, and a topological approach to enable knowledge building and external sensory processing. This paper focuses on the environmental reconstruction module based on topological based approach. The Topological based approach represents the data flow from sensing to knowledge building. We use dynamic density growing neural gas algorithm as the based of reconstruction module. It implies the dynamic granularity of topological structure of reconstructed environment. The module presents continuous real-time environmental reconstruction building from topological information generated by dynamic density growing neural gas. The reconstructed topological map composes as 3-D map nodes position and normal vector of the node, and their edges. We conducted several experiments showing efficient locomotion behavior could be realized using the proposed model for validating our proposed model.

DOI： 10.1109/ies55876.2022.9888288

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/ijcnn55064.2022.9892864

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Publishing type：Research paper (scientific journal)   Publisher：World Scientific Pub Co Pte Ltd  

In this paper, we focused on the medical procedure to insert needles under computer tomography (CT) guidance as a target for the robotization of medical surgery, which is one of Interventional Radiology (IR). IR is a general term for treatments that use devices to visualize patients’ bodies. During surgery, our developed robot, known as Zerobot, specializes in inserting a needle into the patient under CT guidance. Its surgery is less invasive and more effective in treating small cancer tumors because the temperature of the inserted needle tip is controlled. Zerobot was originally designed to be remotely controlled by doctors, and first-in-human feasibility trials in 2018 confirmed its surgical capability. We are currently focusing on the automatic insertion function to reduce the workload of doctors. In an animal experiment, Zerobot was unable to insert the needle into the animal during IR surgery if the needle was bent. As a result, the goal of this research is to have the robot function automatically so that the needle does not bend during surgery. We propose a method for estimating the form of the needle using a force sensor. There are three different types of needle forms. Next, the proposed method distinguishes between these needle forms by measuring the difference in force sensor data when the needle root is moved slightly. In addition, we conducted tests to confirm the efficacy of the proposed method.

DOI： 10.1142/s2424905x22410045

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Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)   Publisher：{MDPI} {AG}  

DOI： 10.3390/app12031705

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DOI： 10.1007/s10015-021-00718-7

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DOI： 10.1007/s10015-021-00720-z

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/MHS56725.2022.10092168

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Other Link： https://dblp.uni-trier.de/db/conf/mhs/mhs2022.html#MayumiMKMHT22

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Fuji Technology Press Ltd.  

Hierarchical topological structure learning methods are expected to be developed in the field of data mining for extracting multiscale topological structures from an unknown dataset. However, most methods require user-defined parameters, and it is difficult for users to determine these parameters and effectively utilize the method. In this paper, we propose a new parameter-less hierarchical topological structure learning method based on growing neural gas (GNG). First, we propose batch learning GNG (BL-GNG) to improve the learning convergence and reduce the user-designed parameters in GNG. BL-GNG uses an objective function based on fuzzy C-means to improve the learning convergence. Next, we propose multilayer BL-GNG (MBL-GNG), which is a parameter-less unsupervised learning algorithm based on hierarchical topological structure learning. In MBL-GNG, the input data of each layer uses parent nodes to learn more abstract topological structures from the dataset. Furthermore, MBL-GNG can automatically determine the number of nodes and layers according to the data distribution. Finally, we conducted several experiments to evaluate our proposed method by comparing it with other hierarchical approaches and discuss the effectiveness of our proposed method.

DOI： 10.20965/jaciii.2021.p1011

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Authorship：Lead author,　Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)  

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER  

The research of humanoid is widely discussed whether by simulations or real machines. In human bipedal walking, swinging arms in opposite directions is a natural movement. In this research, a model of the humanoid robot, including slipping, bumping, surface-contacting and point-contacting of the foot has been established, and its dynamical equation is derived by the Newton-Euler method. And the natural arm-swing simulation has been produced, which showed that the input torque in yaw rotation of the torso could cause natural arm-swing. "Natural" means that the arm-swinging motion is induced by coupling effects existing in nonlinear dynamics of humanoid robot even though no torques have been input into shoulders. Based on the results, a hypothesis that the vibration in the yaw rotation of the torso caused natural arm swing is proposed. In this paper, we compared the arm-swing movement with or without the input torque of yaw rotation of the torso by using the above humanoid robot model. The simulation data proved the hypothesis to be valid.

DOI： 10.1007/s10015-020-00636-0

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J-GLOBAL

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J-GLOBAL

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Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER  

RANdom SAmple Consensus (RANSAC) has been applied to many 3D image processing problems such as homography matrix estimation problems and shape detection from 3D point clouds, and is one of the most popular robust estimator methods. However, RANSAC has a problem related to the trade-off between computational cost and stability of search because RANSAC is based on random sampling. In our previous work, we proposed Adaptive Evolution Strategy SAmple Consensus (A-ESSAC) as a new robust estimator, and we applied ESSAC to the homography matrix estimation for 3D SLAM using RGB-D camera. A-ESSAC is based on Evolution Strategy to maintain the genetic diversity. Furthermore, ESSAC has two heuristic searches. One is a search range control for reducing the computational cost of RANSAC. The other is adaptive/self-adaptive mutation for changing the search strategy of A-ESSAC according to the best fitness value. In this paper, we apply A-ESSAC to 3D reconstruction method using two cameras, and we show an experimental result, and discuss the effectiveness of the proposed method.

DOI： 10.1007/s10015-020-00603-9

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DOI： 10.1007/s10015-020-00606-6

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Over the last decade, the labor shortage suffers various industry sectors due to declining birthrate and aging population. Thus, autonomous robots have been widely researched for overcoming the labor-shortage phenomena. The construction sector is one of the industries that require high manpower to conduct various tasks. Traditional illuminance measurement in construction sites is one of the tasks that require much manpower and consumption time. As such, the development of various robots has been conducted to autonomously measure construction sites' illuminance. In this paper, we develop an autonomous mobile robot to perform illuminance measurement while performing simultaneous localization and mapping (SLAM), and obstacle avoidance. Human operators first set a navigation path and send it to the robot. With the given path, the robot starts to navigate the environment autonomously and measures the illuminance of the target points in the environment. The robot is validated through several experiments conducted in real-world indoor environments. Experimental results showed that the robot was able to autonomously measure the illuminance of the environment.

DOI： 10.1109/ARSO46408.2019.8948806

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

This paper presents the verification of the turbidity tolerance of a stereo-vision-based 3D pose estimation system for underwater docking applications. To the best of the authors' knowledge, no studies have yet been conducted on 3D pose (position and orientation) estimation against turbidity for underwater vehicles. Therefore, the effect of turbidity on the 3D pose estimation performance of underwater vehicles and a method of operating under turbid conditions were studied in this work. A 3D pose estimation method using the real-time multi-step genetic algorithm (RM-GA) proposed by the authors in the previous works shows robust pose estimation performance against changing environmental conditions. This paper discusses how and why the RM-GA is well suited to effective 3D pose estimation, even when turbid conditions disturb visual servoing. The experimental results confirm the performance of the proposed 3D pose estimation system under different levels of turbidity. To demonstrate the practical usefulness of the RM-GA, docking experiments were conducted in a turbid pool and a real sea environment to verify the performance and tolerance of the proposed system under turbid conditions. The experimental results verify the robustness of the system against turbidity, presenting a possible solution to a major problem in the field of robotics.

DOI： 10.1007/s00773-018-0586-7

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Other Link： http://link.springer.com/article/10.1007/s00773-018-0586-7/fulltext.html

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In the application of Reinforcement Learning to real tasks, the construction of state space is a significant problem. In order to use in the real-world environment, we need to deal with the problem of continuous information. Therefore, we proposed a method of the construction of state space using Growing Neural Gas. In our method, the agent constructs a state space model from its own experience autonomously. Furthermore, it can reconstruct the suitable state space model to adapt the complication of the environment. Through the experiments, we showed that Reinforcement Learning could be performed efficiently by successively updating the state space model according to the environment.

DOI： 10.1109/ICMLC48188.2019.8949181

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：FUJI TECHNOLOGY PRESS LTD  

Human posture recognition has been a popular research topic since the development of the referent fields of human-robot interaction, and simulation operation. Most of these methods are based on supervised learning, and a large amount of training information is required to conduct an ideal assessment. In this study, we propose a solution to this by applying a number of unsupervised learning algorithms based on the forward kinematics model of the human skeleton. Next, we optimize the proposed method by integrating particle swarm optimization (PSO) for optimization. The advantage of the proposed method is no pre-training data is that required for human posture generation and recognition. We validate the method by conducting a series of experiments with human subjects.

DOI： 10.20965/jaciii.2019.p0519

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In this paper, an episodic memory learning framework is proposed for categorizing and encoding sensory information that acquired from a robot for environment adaptation and sensorimotor map building. The proposed learning model termed as Incremental Episodic Memory Adaptive Resonance Theory (In-EMART), consists two layers of ART networks which used to detect novel event encountered by the robot and learn the spatio-temporal relationship by creating neurons incrementally. A set of connected episodes forms a sensorimotor map that can be used for path planning and goal navigation autonomously. The experimental results for a mobile robot show that: (i) In-EMART can learn sensory data in real time which is important for robot implementation; (ii) the model solves the perceptual aliasing issue by recalling the connected episode neurons; (iii) compared with previous works, the proposed method further generates a sensorimotor map for connecting episodes together to navigate from one place to another continuously.

DOI： 10.1109/KCIC.2018.8628468

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC  

In this paper, an unsupervised learning model of episodic memory is proposed. The proposed model, enhanced episodic memory adaptive resonance theory (EEM-ART), categorizes and encodes experiences of a robot to the environment and generates a cognitive map. EEM-ART consists of multilayer ART networks to extract novel events and encode spatio-temporal connection as episodes by incrementally generating cognitive neurons. The model connects episodes to construct a sensorimotor map for the robot to continuously perform path planning and goal navigation. Experimental results for a mobile robot indicate that EEM-ART can process multiple sensory sources for learning events and encoding episodes simultaneously. The model overcomes perceptual aliasing and robot localization by recalling the encoded episodes with a new anticipation function and generates sensorimotor map to connect episodes together to execute tasks continuously with little to no human intervention.

DOI： 10.1109/TCDS.2018.2875309

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

This paper proposes a real-time topological structure learning method based on concentrated/distributed sensing for a 2D/3D point cloud. First of all, we explain a modified Growing Neural Gas with Utility (GNG-U2) that can learn the topological structure of 3D space environment and color information simultaneously by using a weight vector. Next, we propose a Region Of Interest Growing Neural Gas (ROI-GNG) for realizing concentrated/distributed sensing in real-time. In ROI-GNG, the discount rates of the accumulated error and utility value are variable according to the situation. We show experimental results of the proposed method and discuss the effectiveness of the proposed method.

DOI： 10.1007/978-3-030-27535-8_8

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

This paper presents a method with dynamic density topological structure generation for low-cost real-time vertical ladder detection from 3D point cloud data. Dynamic Density Growing Neural Gas (DD-GNG) is proposed to generate a dynamic density of the topological structure. The density of the structure and the number of nodes will be increased in the targeted object area. Feature extraction model is required to classify suspected objects for being processed in the next time process. After that, rungs of the vertical ladder is processed using an inlier-outlier method. Thus, the ladder detection model represents the ladder with a set of nodes and edges. Next, affordance detection is processed for detecting the feasible grasped location. To validate the effectiveness of the proposed method, a series of experiments are conducted on a 4-legged robot with a non-GPU board for real-time vertical ladder detection and climbing to validate the effectiveness of the proposed method. Results show that our proposed method able to detect and track the ladder structure in real-time with a much lower computational cost. The affordance of the ladder provides safety information for robot grasping.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

This paper presents the novel capabilities of a quadruped robot by performing horizontal-vertical-horizontal movement transition through vertical ladder without handrailing supporter. To overcome the proposed problem, we propose a multi-behavior generation model using independent stepping and pose control in the quadruped robot. The model is able to generate appropriate behavior depending on external (3D point clouds) and internal sensors (ground touch sensor, and Inertial Measurement Unit). Posture condition, safe movement area, possible touchpoint, grasping possibility, and target movement are the information that is analyzed from the sensors. There are four options developed in behavior generation, which are, Approaching, Body Placing, Stepping, and Grasping behavior. In order to prove the effectiveness of the proposed algorithm, the model was implemented on the computer simulation and the real application. Before being applied in the real robot, the proposed model is optimized in the computer simulation. Then, the optimized parameter is used for applying in the real robot. As a result, the robot succeeded to move through the ladder without handrail from lower stair to upper stair. From the analysis, the body placing behavior is the most important strategy in the proposed case.

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Language：English   Publishing type：Part of collection (book)   Publisher：Springer International Publishing  

This chapter introduces a novel four-limbed robot, WAREC-1, that has advanced locomotion and manipulation capability with versatile locomotion styles. At disaster sites, there are various types of environments through which a robot must traverse, such as rough terrain filled with rubbles, narrow places, stairs, and vertical ladders. WAREC-1 moves in hazardous environments by transitioning among various locomotion styles, such as bipedal/quadrupedal walking, crawling, and ladder climbing. WAREC-1 has identically structured limbs with 28 degrees of freedom (DoF) in total with 7-DoFs in each limb. The robot is 1,690 mm tall when standing on two limbs, and weighs 155 kg. We developed three types of actuator units with hollow structures to pass the wiring inside the joints of WAREC-1, which enables the robot to move on rubble piles by creeping on its stomach. Main contributions of our research are following five topics: (1) Development of a four-limbed robot, WAREC-1. (2) Simultaneous localization and mapping (SLAM) using laser range sensor array. (3) Teleoperation system using past image records to generate a third-person view. (4) High-power and low-energy hand. (5) Lightweight master system for telemanipulation and an assist control system for improving the maneuverability of master-slave systems.

DOI： 10.1007/978-3-030-05321-5_7

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Publishing type：Research paper (scientific journal)   Publisher：Japan Society of Mechanical Engineers  

DOI： 10.1299/transjsme.18-00254

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Aiming at developing underwater battery recharging system, the author developed a docking system using stereo-vision-based visual servoing and a 3D marker. The 3D marker consists of red, green, blue spheres that do not emit the light which is named as a passive marker. Real-time relative pose (position and orientation) estimation was implemented utilizing the 3D model-based matching method and real-time multistep genetic algorithm (RM-GA). Given the situation that the docking aims for battery recharging in the deep-sea bottom, the pitch-dark and turbid environment should be considered as an inevitable condition for battery recharging. In our previous works, the docking experiments were conducted in the actual sea, having verified the effectiveness of the proposed system using the passive 3D marker in the daytime environment with turbid water condition. Since lighting passive 3D marker by light from the vehicle in turbid water environment results in a situation that the images taken by video cameras set on the vehicle were looked wholly white, some new idea seems to be required. To overcome this difficulty, the newly lighting 3D marker (active 3D maker) has LEDs inside was introduced in the previous work. The main objective of this study is to check the feasibility area of the proposed system for the docking application, comparison of recognition performance using the active and passive 3D marker that was conducted in the simulated pool with the turbid water is focused. And then, the experiment using the active 3D marker in the actual sea has been performed. The experimental results have confirmed that the new active 3D marker with no-lighting from the vehicle could be recognizable in dark and turbid environment than the passive 3D marker with the lighting from the vehicle.

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Institute of Electrical and Electronics Engineers Inc.  

This paper presents a natural mechanism of the human brain for generating a dynamic path planning in 3-D rough terrain. The proposed paper not only emphasizes the inner state process of the neuron but also the development process of the neurons in the brain. There are two algorithm processes in this proposed model, the forward transmission activity for constructing the neuron connections to find the possible way and the synaptic pruning activity with backward neuron transmission for finding the best pathway from current position to target position and reducing inefficient neuron with its synaptic connections. In order to respond and avoid the unpredictable obstacle, dynamic path planning is also considered in this proposed model. An integrated system for applying the proposed model in the real cases is also presented. In order to prove the effectiveness of the proposed model, we applied it in the pathway of a four-legged robot on rough terrain in both computer simulation and real cases. Unpredictable collision is also performed in those experiments. The model can find the best pathway and facilitate the safe movement of the robot. When the robot found an unpredictable collision, the path planner dynamically changed the pathway. The proposed path planning model is capable to be applied in further advance implementation.

DOI： 10.1109/TCDS.2017.2711013

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Institute of Electrical and Electronics Engineers Inc.  

Along with the advancement of research on intelligent robotics, the situation where the robot moves independently and performs work in real environment is expanding. In such situation, it is one of the highest priority for the autonomous mobile robot to reach the destination point without failure. Therefore, we consider the transfer of the mobile robot on pre-planned paths to a partially observed environment. When the mobile robot encounters danger in the observing environment and determines to take a detour, it is necessary to re-plan safe routes in a short time. In this study, we focused on the rollover risk of mobile robot. And we simulate the autonomous rerouting of the mobile robot for finding a more secure route such that it can safely arrive at the destination point, whenever it senses high possibility of rollover. By using fuzzy inference to judge rollover risk, the mobile robot judges the necessity of route change according to the magnitude of risk. We also aimed to quickly perform rerouting by using the D∗ Lite algorithm in real-time for robot movement. We propose a method to realize route planning modification based on evaluation and judgment of rollover risk by combining fuzzy inference and D∗ Lite algorithm. As a result, we confirm that the autonomous mobile robot can reach the destination point by real-time evaluation of the risk and taking detour action as necessary. Experiments are conducted through computer simulation using a virtual mobile robot and a 3D path based on graph theory. Finally, we discuss about the result of the simulation.

DOI： 10.1109/SSCI.2017.8285367

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In the application of Reinforcement Learning to real tasks, a state space construction is an important problem. In order to use in real world environment, we need to deal with the problem of continuous information. Therefore, we proposed a Growing Neural Gas method based on state space construction model. In our system, the agent constructs State Space Model from its own experience autonomously. Furthermore, it can reconstruct a suitable state space to adapt complication of the environment. Through the experiments, we showed that our method using state space performs as well as the conventional method by using a smaller number of states.

DOI： 10.1109/SCIS-ISIS.2018.00220

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Recently, the expectation to teleoperated mobile robots has been increasing much in order to perform the monitoring in various scenes. However, there are many critical problems in the teleoperated mobile robots. In this paper, we discuss cooperative formation behavior of teleoperated multiple robots. Especially, we focus on an enclosing formation behavior of a target object. First, we define the problem setting of the enclosing formation behavior. In our method, the enclosing formation is divided by two strategies in order to reduce the search space of robot poses. Next, we introduce Batch Learning Growing Neural Gas (BL-GNG) in order to improve the learning convergence and reduce the user-designed parameters in GNG. BL-GNG uses an objective function based on Fuzzy C-means for improving the learning convergence. Furthermore, we apply two-layers BL-GNG to decide the positions of enclosing formation. Finally, we show several experimental results of the proposed method.

DOI： 10.1109/SMC.2018.00149

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Human actions can be realized by observing the trajectories of skeleton joints. In this paper, we propose an unsupervised episodic memory learning model for skeleton based action learning and recognition. The proposed model, Multi-channel Episodic Memory Adaptive Resonance Theory (McEMART), consists of three layers: short term memory, working memory and Episodic memory. The short term memory layer is formed by multiple ART networks to obtain sensory data and cluster them into neurons in working memory layer. Instead of obtaining the whole skeleton as an input, we divide the human skeleton into three parts, upper part body, main body and lower part body. Each of them is then feed to McEM-ART short term memory layer for learning. Episodic memory layer extracts novel events and encodes spatio-temporal connection between them as episodes by generating cognitive neurons incrementally for action recognition. Comparing with previous works, McEM-ART further integrates a novel memory anticipation functions for encoding crucial events and episodes and recalling them using partial and inexact cues. Experimental results demonstrate that McEM-ART is capable of clustering human skeleton data into event neurons, encoding sequence of activation events as episode neurons for action recalling and recognition.

DOI： 10.1109/SMC.2018.00151

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

The current world is full of automatic systems, they can be found in almost every where. A good system provides not only the convince, but also the curious and interest about the technology. This paper provide an integrated robot control system for controlling the movement of spatial robot towards the desired position, and this system can be applied for automatic nursing such as in kindergarten or nursing. The experiment shows that it provides a stable and reliable surveillance ability, and keeps a low cost at the same time.

DOI： 10.23919/WAC.2018.8430398

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

Recently, in order to reduce secondary disasters at the disaster site, the expectation of rescue robots has been increasing. In the disaster site, many kinds of environments are mixed. Therefore, in order to make robots perform the environmental sensing in the complicated environment, it is necessary to change the measurement area and density according to the environment information. Therefore, we developed a Variable Ranging Sensor Array as a 3D-distance measurement system for changing measurement area and density. In this paper, we propose an intelligent control method for the sensor array based on Multi-Objective Behavior Coordination, and show results of proposed method in a simulation experiment.

DOI： 10.1007/978-3-319-97586-3_35

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

This paper presents a new integration model for on-line topological map building with environment slow features detection. The proposed model is formed by the integration of Bayesian Adaptive Resonance Associative Memory (BARAM) and Incremental Slow Feature Analysis (IncSFA). IncSFA incrementally extracts slowly varying features from a rapidly changing input signal. These slow features will be fed to BARAM for environment learning to build a topological map. The explored environment is represented as a set of neurons (nodes) and edges that connecting all nodes. Each neuron represents a distinct place and edges store robot traverse information that leads the robot to travel from one node to another. The proposed model is an unsupervised learning technique that does not require any prior knowledge of what an environment is supposed to be for ease of implementation. The effectiveness of our proposed method is validated by several standardized benchmark datasets.

DOI： 10.1109/MHS.2018.8887062

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Since it is difficult to obtain accurate environmental maps at the disaster site, an autonomous mobile robot moves to the destination while updating the environmental map based on the information observed by the equipped sensors. Therefore, it is a difficult task to return safely to the departure point after the robot reached the destination point and completed the instructed task. In this study, we propose a method for an autonomous mobile robot to return safely from the destination point to the departure point in static partially unknown environment. On the outward way, the robot updates the environmental map using traveling risk evaluation scores quantifying the safety of the terrain. Then, in the return way planning, the optimum route is searched using the distance and the risk evaluation score as indices. We implement the combination of A* algorithm and iterative search method for path planning. In order to validate the effectiveness of the proposed method, computer simulation using the virtual environment was performed.

DOI： 10.1109/SCIS-ISIS.2018.00219

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Disturbances of turbidity and low illuminance are problems in real sea areas when recognizing objects with cameras. Therefore, the recognition target was made to emit light so that it can be recognized correctly even in that environment. However, a suitable light intensity of the target was not decided and it is obvious that recognition results was changed by light intensity of the target. This paper presents the analysis of recognition accuracy of the Real-time 3D estimation system by changing the current value of each color LED (red, green, blue) under turbid and low illuminance. Recognition experiments were conducted at the distance 600 [mm] between the ROV and 3D marker. The turbidity level was set constant value. The current value was changing from 0 [mA] to 16 [mA] for each LED individually. The best current for each LED was optimized by the fitness value and estimation value of position and orientation. The results showed that the recognition accuracy of the proposed system was improved by using optimized lighting intensity.

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A stereo-vision-based system of autonomous under-water vehicles (AUVs) for sea-bottom docking that enables for battery recharging to extend persistence time of underwater operation has been developed. This paper presents the docking experiment using a developed V-shaped-thruster typed under-water vehicle. A real-time 3D pose (position and orientation) estimation method using a real-time multi-step genetic algorithm (RM-GA) has been proposed by the authors in previous works and used for docking based on 3D recognition as a feedback pose information in real-time, named as 3D Move on Sensing (3D-MoS). Sea docking experiment results have confirmed the functionality and practicality of proposed docking approach using a hovering typed ROV in previous works. Since the hovering typed underwater vehicles are limited in mobilities concerning speed and operational space, verification of the 3D-MoS system using underwater vehicle that has more mobility deem to be meaningful direction for vision-based docking system to expand the utility value of AUVs. Therefore, in this study, control system for a new V-shaped-thruster typed vehicle is developed and docking experiment is conducted. This paper presents the development of the hardware design of V-shaped-thruster typed underwater vehicle and improvement of controlling with consideration of coupled configuration of thrusters.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

This paper presents that our proposed docking system could apply for a new Remotely Operated Vehicle(ROV). The authors have proposed a 3D-perception based Move on Sensing (3D-MoS) system using a new 3D position and orientation (pose) estimation method with dual-eye camera that exploits the parallactic nature that enables reliable 3D pose estimation in real-time, named as "Real-time Multi-step Genetic Algorithm (RM-GA)." We confirmed ROV have conducted docking that assumes charging battery under water by the system, having shown it effective. As a next step, docking experiment using the new ROV were conducted to verify the proposed system apply for new ROV. But, the new ROV is different from a ROV that have used previous experiments, on the point of thruster setting structure. Then, a new control system using Jacobian that shows relationship voltage and velocity was constructed. After it is confirmed the system is effective, docking experiments In the pool have been conducted. In this report, the structure and result of experiments are reported in detail.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Topological structure learning methods are expected for the field of data mining for extracting multiscale topological structures from an unknown dataset. In this paper, we introduce the unsupervised neural network method for topological structure learning method from point clouds for map building. We propose Batch Learning GNG (BL-GNG) in order to improve the learning convergence. BL-GNG uses an objective function based on Fuzzy C-means for improving the learning convergence. Finally, we conduct on several experiments for evaluating our proposed method by comparing to other hierarchical approaches, and discuss the effectiveness of our proposed method.

DOI： 10.1109/MHS.2017.8305188

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：FRONTIERS MEDIA SA  

In this article, we propose a new approach for mobile robot localization and hybrid map building simultaneously without using any odometry hardware system. The proposed method termed as Genetic Bayesian ARAM comprises two main components: (1) steady-state genetic algorithm (SSGA) for self-localization and occupancy grid map building and (2) Bayesian Adaptive Resonance Associative Memory (ARAM) for online topological map building. The model of the explored environment is formed as a hybrid representation, both topological and grid based, and it is incrementally constructed during the exploration process. During occupancy map building, the robot-estimated self-position is updated by SSGA. At the same time, the robot-estimated self-position is transmitted to Bayesian ARAM for topological map building and localization. The effectiveness of our proposed approach is validated by a number of standardized benchmark datasets and real experimental results carried on the mobile robot. Benchmark datasets are used to verify the proposed method capable of generating topological map in different environment conditions. Real robot experiment to verify the proposed method can be implemented in real world.

DOI： 10.3389/frobt.2016.00068

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Fuji Technology Press  

RANdom SAmple Consensus (RANSAC) has been applied to many 3D image processing problems such as homography matrix estimation problems and shape detection from 3D point clouds, and is one of the most popular robust estimator methods. However, RANSAC has a problem related to the trade-off between computational cost and stability of search because RANSAC is based on random sampling. Genetic Algorithm SAmple Consensus (GASAC) based on a population-based multi-point search was proposed in order to improve RANSAC. GASAC can im-prove the performance of search. However, it is sometimes difficult to maintain the genetic diversity in the search if the large size of outliers is included in a data set. Furthermore, a computational time of GASAC sometimes is slower than that of RANSAC because of calculation of the genetic operators. This paper proposes Evolution Strategy SAmple Consensus (ESSAC) as a new robust estimator. ESSAC is based 011 Evolution Strategy in order to maintain the genetic diversity. In ESSAC, we apply two heuristic searches to ESSAC. One is a search range control, the other is adaptive/self-adaptive mutation. By applying these heuristic searches, the trade-off between computational speed and search stability can be improved. Finally, this paper shows several experimental results in order to evaluate the effectiveness of the proposed method.

DOI： 10.20965/jaciii.2016.p0788

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：ELSEVIER SCIENCE BV  

In this paper, a new network is proposed for automated recognition and classification of the environment information into regions, or nodes. Information is utilized in learning the topological map of an environment. The architecture is based upon a multi-channel Adaptive Resonance Associative Memory (ARAM) that comprises of two layers, input and memory. The input layer is formed using the Multiple Bayesian Adaptive Resonance Theory, which collects sensory data and incrementally clusters the obtained information into a set of nodes. In the memory layer, the clustered information is used as a topological map, where nodes are connected with edges. Nodes in the topological map represent regions of the environment and stores the robot location, while edges connect nodes and stores the robot orientation or direction. The proposed method, a Multi-channel Bayesian Adaptive Resonance Associative Memory (MBARAM) is validated using a number of benchmark datasets. Experimental results indicate that MBARAM is capable of generating topological map online and the map can be used for localization. (C) 2015 Elsevier B.V. All rights reserved.

DOI： 10.1016/j.asoc.2015.09.031

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER INT PUBLISHING AG  

This paper proposes a 3D point cloud segmentation method using a reflection intensity of Laser Range Finder (LRF). In this paper, we use LRF and tilt unit for acquiring a 3D point cloud. First of all, we apply Growing Neural Gas (GNG) to the point cloud for learning a topological structure of the point cloud. Next, we proposed a segmentation method based on an intensity histogram that is composed of the nearest data of each node. Finally, we show experimental results of the proposed method and discuss the effectiveness of the proposed method.

DOI： 10.1007/978-3-319-43518-3_33

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

This paper presents Bezier curve based passive neural control applied in bio-inspired locomotion in order to decrease the computational cost implemented for 4 legged animal robot which has 3 joints in each leg. Neural oscillator model is applied for generating the walking pattern in bio-inspired locomotion. Bezier curve based optimization represents passive neural control supported by evolutionary algorithm tor representing the relationship equation between neuron signal and reference joint signal. Passive neural control is implemented in order to reduce the neuron complexity in neuro-based locomotion by controlling 3 joints with one signal without decreasing the performance both in walking pattern and in its stability level, whereas one leg is represented by one motor neuron. Therefore, the 4 legged robot is controlled by 4 motor neurons which have feedback connection with ground and inertial sensor. In order to prove the effectiveness, we implemented the model in computer simulation and in a small 4 legged robot. This model can decrease the computational cost so it is possible to apply the model in either animal or humanoid robot with low frequency processor.

DOI： 10.1109/IROS.2016.7759654

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

This paper proposes a real-time feature extraction and segmentation method for a 3D point cloud. First of all, we apply Growing Neural Gas with Utility (GNG-U) to the point cloud for learning a topological structure. However, the standard GNG-U cannot learn the topological structure of 3D space environment and color information simultaneously. To this end, we then modify the GNG-U algorithm by using a weight vector. we propose a surface feature extraction and segmentation method by efficiently utilizing the topological structure. Our segmentation method is based on a region growing method whose similarity value uses the inner value of two normal vectors connected by the topological structure. We show experimental results of the proposed method and discuss the effectiveness of the proposed method.

DOI： 10.1109/HSI.2016.7529667

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

Simultaneous Localization and Mapping (SLAM) is one of the most important capabilities for autonomous mobile robots, and many researches have been proposed demonstrating the effective SLAM methods. However, these SLAM methods sometimes require assumptions such as the sensor model, which is difficult to implement and use the SLAM methods. In our previous work, a SLAM method based on Evolution Strategy (ES) was proposed and the on-line SLAM in indoor environments was realized. However, the definition of the map building method was not clear. Therefore, we propose a SLAM method based on a simple map building and search method. In this paper, we explain our autonomous mobile robot system and propose our SLAM method based on (mu+1)-ES. The experimental results show the effectiveness of the proposed method.

DOI： 10.1007/978-3-319-22873-0_6

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

This paper presents a new framework for mobile robot to perform localization and build topological-metric hybrid map simultaneously. The proposed framework termed as Genetic Bayesian ARAM consists of two main components: 1) Steady state genetic algorithm (SSGA) for self-localization and occupancy grid map building and 2) Bayesian Adaptive Resonance Associative Memory (ARAM) for topological map building. The proposed method is validated using a mobile robot. Result show that Genetic Bayesian ARAM capable of generate hybrid map online and perform localization simultaneously.

DOI： 10.1109/SSCI.2015.48

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Recently, robust estimators are expected in various fields such as signal processing and machine learning. In our previous work, we proposed Evolution Strategy Sample Consensus (ESSAC) as a new robust estimator method and improved a trade off between a calculation time and stability of SAmple Consensus (SAC) algorithms. In this paper, we show several experiments for behavior analysis of ESSAC in order to discuss why ESSAC enable to search stably in the dataset including the huge number of noises. and analyze several experiments related with the fitness function of SAC and the behavior of ESSAC.

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC  

This paper proposes a localization method using multiresolution maps for the navigation of multiple mobile robots based on formation behaviors. The remote control of multiple mobile robots is one the most important tasks in robotics to realize distributed remote monitoring in unknown and/or dynamic environments. However, it is very difficult for a human operator to control multiple mobile robots separately at the same time. Therefore, autonomous formation behaviors of multiple robots are required to reduce mental and physical loads of the human operator. If each mobile robot can estimate the self-position or relative position in a group, it is easier for multiple mobile robots to realize formation behaviors. First, we propose a method of simultaneous localization and mapping based on a grid approach. Next, we explain how to share the build map among multiple mobile robots, and propose a self-localization method based on multiresolution maps. Furthermore, we explain the formation behaviors of multiple mobile robots. Finally, we show several experimental results, and discuss the effectiveness of the proposed method.

DOI： 10.1109/TII.2013.2261306

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

This paper proposes a localization method using shared information for teleoperation of multiple robots based on formation behaviors. The localization is one of the most important capabilities. However, other robots can be considered as unknown objects when a mobile robot performs initial self-localization. It is difficult to estimate the initial position if the other robots enter the sensing range of the mobile robot. However, the robot can perform the initial self-localization more efficiently by sharing the map information and each self-position. Therefore, we propose a method of effective initial self-localization using the position information of the other mobile robots and the subtracted measurement data of LRF for multi-robot systems. Finally, we show several experimental results, and discuss the effectiveness of the proposed method.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：International Federation of Automatic Control  

This paper proposes a method of attention allocation for multi-modal perception of humanfriendly robot partners based on various types of sensors built in a smart phone. First, we propose human and object detection method using octagonal templates based on evolutionary robot vision. Next, we propose an integration method for estimating human behaviors based on the human detection using color image by the multi-layered spiking neural network using the time series of positions of human and object. Furthermore, we propose a method of attention allocation based on the time series of human behavior recognition. Finally, we show several experimental results of the proposed method, and discuss the future direction on this research. Copyright © 2013 IFAC.

DOI： 10.3182/20130811-5-US-2037.00054

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

This paper proposes a method of feature extraction from 3D point clouds for informationally structured space including sensor networks and robot partners for co-existing with people. The informationally structured space realizes the quick update and access of valuable and useful information for both people and robots on real and virtual environments. Our method is based on Hierarchical Growing Neural Gas (HGNG). This method is one of self-organizing neural network based on unsupervised learning First, we propose 3D map building method using Kinect in order to acquire the 3D point clouds. Next, we propose the method of the feature extracting method based on HGNG. Finally, we show experimental results of the proposed method and discuss the effectiveness of the proposed method.

DOI： 10.1109/IJCNN.2013.6706825

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

This paper proposes a method of constituting the formation of a multi-robot system for exploration and monitoring. First, we apply a method of multi-objective behavior coordination for integrating behavior outputs from the fuzzy control for collision avoidance and target tracing. Second, we apply a spring model to calculate the temporary target position of each robot for the formation behavior. Third, we constitute monitoring formation, which is realized by enclosing behavior of multi-robot. Finally, we discuss the effectiveness of the proposed method through several simulation results.

DOI： 10.1109/RVSP.2013.24

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC  

This paper proposes a multimodal communication method for human-friendly robot partners based on various types of sensors. First, we explain informationally structured space to extend the cognitive capabilities of robot partners based on environmental systems. Next, we discuss the suitable measurement range for recognition technologies of touch interface, voice recognition, human detection, gesture recognition, and others. Based on the suitable measurement ranges, we propose an integration method to estimate human behaviors based on the human detection using color image and 3-D distance information, and gesture recognition by the multilayered spiking neural network using the time series of human-hand positions. Furthermore, we propose a conversation system to realize the multimodal communication with a person. Finally, we show several experimental results of the proposed method, and discuss the future direction of this research.

DOI： 10.1109/TSMCC.2012.2213810

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Tokyo  

The paper deals with simultaneous optimization of path planning of mobile robots and flow shop scheduling problem. The goal of the path planning problem is to determine an optimal collision-free path between a start and a target point for a mobile robot in an environment surrounded by obstacles. The objective is to minimize the path length without colliding with an obstacle. On the other hand, shop scheduling problems deal with processing a given set of jobs on a given number of machines. Each operation has an associated machine on which it has to be processed for a given length of time. The problem is to minimize the overall time demand of the whole process. In this paper, we deal with two robots carrying items between the machines. Bacterial memetic algorithm is proposed for solving this combined problem. The algorithm is verified by experimental simulations and compared to classical techniques.

DOI： 10.1007/s10015-012-0021-9

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER HEIDELBERG  

The goal of the path planning problem is to determine an optimal collision-free path between a start and a target point for a mobile robot in an environment surrounded by obstacles. This problem belongs to the group of combinatorial optimization problems which are approached by modern optimization techniques such as evolutionary algorithms. In this paper the bacterial memetic algorithm is proposed for path planning of a mobile robot. The objective is to minimize the path length and the number of turns without colliding with an obstacle. The representation used in the paper fits well to the algorithm. Memetic algorithms combine evolutionary algorithms with local search heuristics in order to speed up the evolutionary process. The bacterial memetic algorithm applies the bacterial operators instead of the genetic algorithm's crossover and mutation operator. One advantage of these operators is that they easily can handle individuals with different length. The method is able to generate a collision-free path for the robot even in complicated search spaces. The proposed algorithm is tested in real environment.

DOI： 10.1007/s12293-012-0076-0

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

The localization is one of the most important capabilities for mobile robots. However, other robots can be considered as unknown objects when a mobile robot performs localization, because other robots can enter the sensing range of a mobile robot. Therefore, we propose a method of intelligent selflocalization using evolutionary computation for multiple mobile robots based on simultaneous localization and mapping (SLAM). First, we explain the method of SLAM using occupancy grid mapping by a single mobile robot. Next, we propose an intelligent self-localization method using multi-resolution map and evolutionary computation based on relative position of other robots in the sensing range. The experimental results show the effectiveness of the proposed method. © Springer-Verlag Berlin Heidelberg 2012.

DOI： 10.1007/978-3-642-33509-9_22

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The goal of the path planning problem is to determine an optimal collision-free path between a start and a target point for a mobile robot in an environment surrounded by obstacles. In case of probabilistic environment not only static obstacles obstruct the free passage of the robot, but there are appearances of obstacles with probability. The problem is approached by the bacterial memetic algorithm. The objective is to minimize the path length and the number of turns without colliding with an obstacle. Our method is able to generate a collision-free path in probabilistic environment. The proposed algorithm is tested by simulations. © Springer-Verlag Berlin Heidelberg 2012.

DOI： 10.1007/978-3-642-29934-6_42

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Recently, tele-operation using multi-robot system has been discussed from various viewpoints of shared autonomy, intelligent control, formation behaviors, and information visualization. Furthermore, tele-operation systems have been applied to the remote monitoring of dangerous areas for people such as deep seas, nuclear power plants, and disaster-stricken areas. In this paper, we focus on the structured intelligence based on interconnected intelligent functions of map building, localization of mobile robots, 3D reconstruction and visualization, and human-friendly interface for tele-operation. © 2011 IEEE.

DOI： 10.1109/MHS.2011.6102177

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ALIFE ROBOTICS CO, LTD  

The paper deals with simultaneous optimization of path planning of mobile robots and flow shop scheduling problem. The goal of the path planning problem is to determine an optimal collision-free path between a start and a target point for a mobile robot in an environment surrounded by obstacles. The objective is to minimize the path length without colliding with an obstacle. On the other hand, shop scheduling problems deal with processing a given set of jobs on a given number of machines. Each operation has an associated machine on which it has to be processed for a given length of time. The problem is to minimize the makespan, i.e., the overall time demand of the whole process. In this paper we deal with two robots carrying items between the machines. Bacterial memetic algorithm is proposed for solving the problem.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IOS PRESS  

Recently, multiple mobile robots have been used for exploration and remote monitoring in unknown areas. The required functions for human-friendly remote monitoring and control are (1) Extraction of human intention, (2) Semi-autonomous tele-operation, (3) Multi-robot formation behaviors, (4) Sensor fusion for information extraction, (5) Perception of situation, (6) Decision support based on information visualization and (7) Extraction of human interest. We proposed an intelligent navigation system for human-friendly remote monitoring and control. The intelligent navigation system for multiple mobile robots is composed of simultaneous localization and mapping (SLAM), intelligent path planning based on multi-resolution map, 3D information visualization system, and human navigation system based on touch interface. In this chapter, we focus on the information visualization and map building for remote monitoring and control of multiple mobile robots, and show several experimental results of the proposed methods.

DOI： 10.3233/978-1-61499-092-5-120

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This paper discusses the multi-modal communication for robot partners based on computational intelligence in informationally structured space. First, we explain recognition methods of touch interface, voice recognition, human detection, gesture recognition used in the multi-modal communication. Furthermore, we propose a conversation system to realize the multi-modal communication with a person. Finally, we show several experimental results of the proposed method, and discuss the future direction on this research. © 2012 IEEE.

DOI： 10.1109/GCCE.2012.6379611

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

This paper proposes a method for 3D modeling of environments used to perform teleoperation of a mobile robot. Recently, the expectation to tele-operated mobile robots has been increasing much in order to perform a monitoring in various scenes. However, there are many critical problems in teleoperated systems. Especially, we must expand visual range from a robot, the usability of human interface, and intention sharing between the robot and operator. First, we discuss information visualization for human-friendly tele-operation. Next, we propose a tele-operating system based on multi-resolution map. Finally, we propose a method of 3D modeling using Microsoft Kinect sensor, and show several experimental results of the proposed method.

DOI： 10.1109/CEC.2012.6253007

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

This paper proposes a method for constituting the formation of a multi-robot system according to dynamically changing environments. First, we apply a method of multi-objective behavior coordination for integrating behavior outputs from the fuzzy control for collision avoidance and target tracing. Second, we apply a spring model to calculate the temporary target position of each robot for the formation behavior. Third, we discuss multi-robot behaviors based on the concept of coupling. The tight coupling is realized by the spring model while the loose coupling is realized by the individual decision making based on connection and disconnection with other robots. Furthermore, the proposed method is applied to the exploration in unknown environments. Finally, we discuss the effectiveness of the proposed method through several simulation results.

DOI： 10.1109/FUZZ-IEEE.2012.6251150

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Simultaneous localization and mapping (SLAM) is one of important topics in robotics. However, we must consider various intelligent behaviors in SLAM, e.g., the exploration of unknown areas and effective path planning of mobile robots. To realize these intelligent behaviors, we use a multi-resolution map. The multi-resolution map can be updated by the operators suitable to a specific aim. The first aim is to represent the occupied or empty cells in the built map. The next aim is to represent the unknown areas in the built map. These are used for the intelligent planning. The intelligent planning is composed of preplanning, online planning, and adaptive planning. These planning methods are used according to the state of a built map. The experimental results show the effectiveness of the proposed method. © 2011 IEEE.

DOI： 10.1109/RIISS.2011.5945794

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

This paper discusses measurement methods of human behaviors based on sensor network and human interaction of rehabilitation using robot partners. First, we explain robot partners and sensor networks for rehabilitation. Next, we apply a steady-state genetic algorithm to extract human motions from 3D distance image. Finally, we discuss the effectiveness of the proposed methods through several experimental results.

DOI： 10.1109/FUZZY.2011.6007712

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Recently, the expectation to tele-operated mobile robots has been increasing much in order to perform the monitoring in various scenes. However, there are many critical problems in tele-operated system. Especially, we must improve the restriction of visual range, the usability of human interface, and intention sharing with the operator. In this paper, we discuss the monitoring system of a tele-operated robot and human interface based on visual information and distance information from the tele-operated mobile robots. Next, we propose a method of a navigation system based on multi-featured visualization. Finally, we propose a tele-operated system using iPhone, and show several experimental results of the proposed method. © 2011 IEEE.

DOI： 10.1109/RIISS.2011.5945796

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

The goal of the path planning problem is to determine an optimal collision-free path between a start and a target point for a mobile robot in an environment surrounded by obstacles. This problem belongs to the group of hard optimization problems which can be approached by modern optimization techniques such as evolutionary algorithms. In this paper the bacterial memetic algorithm is proposed for path planning of a mobile robot. The representation used in the paper fits well to the algorithm. Memetic algorithms combine evolutionary algorithms with local search heuristics in order to speed up the evolutionary process. The bacterial memetic algorithm applies the bacterial operators instead of the genetic algorithm's crossover and mutation operator. One advantage of these operators is that they easily can handle individuals with different length. The proposed algorithm is tested in real environment. © 2011 IEEE.

DOI： 10.1109/RIISS.2011.5945787

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Recently, multi-robot systems have been discussed to realize a large size of distributed autonomous system. Furthermore, multi-robot systems have been applied to various problems such as autonomous guided vehicles, soccer robots, and search and rescue system by multi-robot. This paper proposes intelligent formation behavior for the multi-robot based on sensor fusion. First, we discuss multi-agent systems and wireless network technologies. Next, we explain the hardware specification of robot and tele-operated system and wireless communication. Finally, we show experimental results, and discuss the availability of intelligent formation behavior for multi-robot.

DOI： 10.1109/FUZZY.2011.6007709

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Publishing type：Research paper (international conference proceedings)  

Recently, tele-operation has been used for various application fields such as remote monitoring for public areas and rescues in disasters. This paper discusses the capability of communication by movable people using smart phones in case of disasters through computer simulations, and visualization of disasters and information reliability based on map building through wireless communication. First, we discuss the tele-operation based on robot technology, information technology, communication technology, and intelligent technology. Next, we propose a computer simulation model of wireless communication among unmovable and movable people using smart phones. Next, we propose a visualization method based on map building through wireless emergency communication. Finally, we show several preliminary simulation results, and discuss the effectiveness of smart phones using wireless emergency communication.

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Path planning is one of important topics in robotics. When the mobile robot is building the map, it is better to use the higher resolution map in order to build a precise map. However, after the map building, if the resolution of the built map is very high, it takes much computational memory and time to perform the path planning. To realize the effective path planning, we use a multi-resolution map. The multi-resolution map can be updated by the operators suitable to a specific aim. The first aim is to represent the occupied or empty cells in the built map. The next aim is to represent the unknown areas in the built map. These are used for the path planning. The path planning is based on potential field method. The path planning uses steady-state genetic algorithm in order to set up the sub-target points. The experimental results show the effectiveness of the proposed method. © 2011 SICE.

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Publishing type：Article, review, commentary, editorial, etc. (scientific journal)   Publisher：The Robotics Society of Japan  

DOI： 10.7210/jrsj.40.475

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Language：Japanese   Publisher：The Japan Society of Mechanical Engineers  

From the viewpoint of disaster prevention, it is expected the unmanned automation of daily inspection/health monitoring and diagnostic work for facilities/structures in places where it is difficult to let the inspectors going such as offshore plant and dangerous places. By adopting remote controlled/autonomous robot, regarding the occurrence of abnormality due to human factors, breakage of facilities due to aging of facilities, malfunction caused by them, accidents by increasing the frequency of periodic inspection, it becomes possible to prevent it in advance. In addition, introduction of the robot makes it possible to conduct inspection work even during operation, so it is expected that the availability factor of facilities in such environment will be improved. This paper described about the plant disaster prevention challenge of the World Robot Summit 2020 disaster robotics category held at the Fukushima Robot Test Field in October 2021. The competition concept and the competition rule were introduced. The analysis of the competition results were shown and considered based on competition results. Finally, the lessons learned was mentioned.

DOI： 10.1299/jsmermd.2022.1a1-h02

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Language：Japanese   Publisher：The Japan Society of Mechanical Engineers  

<p>In recent years, interventional radiology (IR) which is a medical procedure has been attracting considerable attention. Since this surgical method is less invasive, the number of this surgery tends to increase. However, doctors are exposed to strong radiation in the case under CT-guidance. In order to overcome this problem, we developed remote-controlled IR assistance robot. When doctors operate by remote control, there is possibility that parts of the robot collide with peripheral devices or a patient. Especially, if in the case of collision with the patient, it could become a serious incident. Accordingly, it is important to build a suitable path plan of the needle insertion before the surgery. Doctors, however, currently, do not have methods to grasp the possibility of the collision. Therefore, a simulation which detect collision with a patient in the advanced planning is proposed in this paper. The effectiveness of proposed simulation is confirmed.</p>

DOI： 10.1299/jsmermd.2020.2A1-E06

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Publisher：The Japan Institute of Marine Engineering  

DOI： 10.5988/jime.54.821

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Language：Japanese   Publisher：The Japan Society of Mechanical Engineers  

<p>Recently, large-scale disasters occur frequently, and the demand for disaster robots is increasing. Environmental perception is necessary for mobile robots to act autonomously in complex environments such as disaster sites. Various measurements are necessary for environmental perception in such environments. For example, global measurement for recognizing the entire surrounding environment, local measurement for recognizing an obstacle, and the like are included. Therefore, in this research, we develop a Variable Ranging Sensor Array equipped with multiple Laser Range Finders. In this paper, we propose a control method of the sensor array measurement using the concept of Multi-Objective Behavior Coordination. We showed the effectiveness of the proposed method by experiment that moved the mobile robot autonomously based on the environmental perception result from the sensor array.</p>

DOI： 10.1299/jsmermd.2019.2P1-D02

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Language：Japanese   Publisher：The Japan Society of Mechanical Engineers  

<p>In recent years, interventional radiology (IR) which is a medical procedure has been attracting considerable attention. Since this surgical method is less invasive, but doctors are exposed to strong radiation in the case under CT-guidance. In order to overcome this problem, we developed remote-controlled IR assistance robot. As a research topic, automated needle puncturing by the robot is focused on. However, currently the robot cannot obtain CT image in real time. So, the robot cannot obtain the form of needle based on CT image and cannot control state of needle accurately. On the other hands, we aim that the robot moves automatically for restraining a bend of needle. As the first step for this, the method to estimate a form of needle by using a force sensor information is proposed in this paper. If a form of the needle is displayed in user interface, it become easy for doctors to remove a bend of the needle during surgery with remote-controlled IR assistance robot. Additionally, we conducts the experiment to confirm effectiveness of proposed method.</p>

DOI： 10.1299/jsmermd.2019.1P1-A02

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Publishing type：Article, review, commentary, editorial, etc. (scientific journal)  

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Language：Japanese   Publisher：The Japan Society of Mechanical Engineers  

<p>Recently, the expectation to rescue robots has been increasing much in order to perform the monitoring in disaster areas. However, there are many critical problems in rescue robots. Especially, a real-time 3D localization is the one of the most important capability for the rescue robot by using 3D measurement sensor. In this paper, we introduce our Sokuiki sensor array system for measuring 3D distance data. Next, we propose Evolution Strategy (ES) based real-time 3D localization method using Sokuiki sensor array. Our method uses the occupancy grid map for the map representation and (μ+1)-ES for estimating the robot pose for realizing the real-time 3D localization. Finally, we show several experimental results of the proposed method.</p>

DOI： 10.1299/jsmermd.2018.2A2-M03

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Language：Japanese   Publisher：The Japan Society of Mechanical Engineers  

Simultaneous Localization And Mapping (SLAM) is one of the most important problems of mobile robot systems. Recently, 3D distance sensor is expected for 3D SLAM. However, It is difficult to build the 3D environmental map in real time because a distance dataset from 3D distance sensor has many position data and it is difficult to search the corresponding points from the dataset in real time. In our research, we combine the 2D environmental maps that are divided into different height for realizing the real-time 3D SLAM. In this paper, we propose a real-time 2D SLAM based on an Evolution Strategy by using 3D distance sensor. The experimental result shows the effectiveness of our proposed method.

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Language：Japanese   Publisher：Japan Society for Fuzzy Theory and Intelligent Informatics  

In this paper, we discuss formation control of multiple mobile robots with co-leader. Our objective is to realize the robust control method for applying the multi-robot system to various situations such as maintenance of the formation shape, obstacle avoidance and adjusting to environmental changes.First, we apply a spring model for constructing the formation and maintaining the optimum position between the robots. Next, we introduce the obstacle avoidance method by using fuzzy control.Finally, we show several experiments in simulation and real environments in order to verify the effectiveness our proposed method.

DOI： 10.14864/fss.31.0_111

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