In this paper, we consider multi-agent system in which every agents have own tasks that differs each other. We propose a method that decreases learning time of reinforcement learning by using the model of environment. In the proposed algorithm, the model is created by sharing the experiences of agents each other. To demonstrate the effectiveness of the proposed method, simulations of a puddle world and experiments of a maze world have been carried out. As a result effective behaviors have been obtained quickly.
en-copyright= kn-copyright= en-aut-name=ItoKazuyuki en-aut-sei=Ito en-aut-mei=Kazuyuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=ImotoYoshiaki en-aut-sei=Imoto en-aut-mei=Yoshiaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=TaguchiHideaki en-aut-sei=Taguchi en-aut-mei=Hideaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=GofukuAkio en-aut-sei=Gofuku en-aut-mei=Akio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Okayama University affil-num=3 en-affil= kn-affil=Okayama University affil-num=4 en-affil= kn-affil=Okayama University END start-ver=1.4 cd-journal=joma no-vol=1 cd-vols= no-issue= article-no= start-page=485 end-page=490 dt-received= dt-revised= dt-accepted= dt-pub-year=2003 dt-pub=20037 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Path evaluation for a mobile robot based on a risk of collision en-subtitle= kn-subtitle= en-abstract= kn-abstract=An odometry system that mobile robot uses for positioning has cumulative error because of wheels' slippage and uneven ground. It causes a risk of collision of obstacles. Therefore, we propose a path evaluation method for a mobile robot based on a risk of collision. To evaluate a robot's path, we define an evaluation value as an integral of a risk of collision along the path. To evaluate the risk of collision at each point, we use an estimated positioning error generated in the odometry system. Using the evaluation method, the robot can plan a path based on a risk of collision, not the shortest path. We also consider sensing points planning for position adjustment of the mobile robot, based on the same approach. Some examples of path evaluation results support a validity of the proposed method.
en-copyright= kn-copyright= en-aut-name=IrieMasahiro en-aut-sei=Irie en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=NagataniKeiji en-aut-sei=Nagatani en-aut-mei=Keiji kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=GofukuAkio en-aut-sei=Gofuku en-aut-mei=Akio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Okayama University affil-num=3 en-affil= kn-affil=Okayama University en-keyword=distance measurement kn-keyword=distance measurement en-keyword=error analysis kn-keyword=error analysis en-keyword=mobile robots kn-keyword=mobile robots en-keyword=navigation kn-keyword=navigation en-keyword=path planning kn-keyword=path planning END start-ver=1.4 cd-journal=joma no-vol= cd-vols= no-issue= article-no= start-page=113 end-page=118 dt-received= dt-revised= dt-accepted= dt-pub-year=2006 dt-pub=20061022 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=A mechanical intelligence in assisting the navigation by a force feedback steering wheel for a snake rescue robot en-subtitle= kn-subtitle= en-abstract= kn-abstract=This paper applies our developed novice users oriented force feedback steering wheel interface and mouse interface to navigating a tank type rescue robot. By analyzing merits and limitation of operating each interface, we propose a combined navigation strategy by the two interfaces. The steering wheel interface consists of a force feedback steering control and a six monitors’ wall. Through this interface, users can navigate the tank robot like driving cars, while watching incoming videos. It provides a daily life operation method for novice users to navigate the tank rescue robot. The steering wheel interface is efficient in exploring open areas. For complex disaster fields, this interface requires users have skillful operation experiences, which take them more attention. The mouse-screen interface consists of a mouse and a camera’s view displayed in a computer screen. Through this interface, users can navigate the tank robot just by mouse clicking. Path planning and low-level controlling are realized by system automatically. The mouse-screen interface can realize exact navigation, especially needed in complex structures, without taking much attention. It gives users more time to care incoming information. The two interfaces can shift into each other at any time. The combined navigation strategy adopts merits of the two interfaces and compensates limitation of each of them. It provides an efficient operation method for novice users to navigate rescue robots.
en-copyright= kn-copyright= en-aut-name=YangZhixiao en-aut-sei=Yang en-aut-mei=Zhixiao kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=ItoKazuyuki en-aut-sei=Ito en-aut-mei=Kazuyuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=HirotsuneKazuyuki en-aut-sei=Hirotsune en-aut-mei=Kazuyuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=SaijoKazuhiko en-aut-sei=Saijo en-aut-mei=Kazuhiko kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=GofukuAkio en-aut-sei=Gofuku en-aut-mei=Akio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=MatsunoFumitoshi en-aut-sei=Matsuno en-aut-mei=Fumitoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Okayama University affil-num=3 en-affil= kn-affil=Okayama University affil-num=4 en-affil= kn-affil=Okayama University affil-num=5 en-affil= kn-affil=Okayama University affil-num=6 en-affil= kn-affil=Okayama University en-keyword=Human Interface kn-keyword=Human Interface en-keyword=Mechancial Intelligence kn-keyword=Mechancial Intelligence en-keyword=Navigation kn-keyword=Navigation en-keyword=Force Feedback Steering Wheel kn-keyword=Force Feedback Steering Wheel en-keyword=Rescue Robot kn-keyword=Rescue Robot en-keyword= Snake Robot. kn-keyword= Snake Robot. END start-ver=1.4 cd-journal=joma no-vol= cd-vols= no-issue= article-no= start-page=113 end-page=118 dt-received= dt-revised= dt-accepted= dt-pub-year=2004 dt-pub=20040920 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=A mechanical intelligence in assisting the navigation by a force feedback steering wheel for a snake rescue robot en-subtitle= kn-subtitle= en-abstract= kn-abstract=We developed a snake rescue robot basing on the proposed mechanical intelligence. The mechanical intelligence is designed to avoid obstacles and to realize desired motions when the robot is navigated by a remote force feedback steering wheel interface. We use free joints to connect modules of the snake robot. Modules can freely turn according to their neighbors. An obstacle-avoiding wheel is mounted on the head of the snake robot. When the head encounters an obstacle, the wheel touches it first to transfer the sliding friction between the wheel and the obstacle into rolling friction, so that the head avoid the obstacle easily. A metal wire is used to link gears mounted on both sides of each module. When any part of the snake robot's body encounters an obstacle, the wire length of each side varies automatically to change the robot's body shape, so that the snake robot avoids the obstacle. The wire length of each side can also be adjusted by a motor. By adjusting the wire length of each side, the snake robot can move in the desired direction. The mechanical intelligence based snake rescue robot has light body, low cost and low computation cost. Experiment results show that the designed mechanical intelligence is effective in realizing desired robot motions together with the force feedback steering wheel interface.
en-copyright= kn-copyright= en-aut-name=YangZhixiao en-aut-sei=Yang en-aut-mei=Zhixiao kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=ItoKazuyuki en-aut-sei=Ito en-aut-mei=Kazuyuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=HirotsuneKazuyuki en-aut-sei=Hirotsune en-aut-mei=Kazuyuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=SaijoKazuhiko en-aut-sei=Saijo en-aut-mei=Kazuhiko kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=GofukuAkio en-aut-sei=Gofuku en-aut-mei=Akio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=MatsunoFumitoshi en-aut-sei=Matsuno en-aut-mei=Fumitoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Okayama University affil-num=3 en-affil= kn-affil=Okayama University affil-num=4 en-affil= kn-affil=Okayama University affil-num=5 en-affil= kn-affil=Okayama University affil-num=6 en-affil= kn-affil=University of Electro-Communications en-keyword=Human Interface kn-keyword=Human Interface en-keyword=Mechancial Intelligence kn-keyword=Mechancial Intelligence en-keyword=Navigation kn-keyword=Navigation en-keyword=Force Feedback Steering Wheel kn-keyword=Force Feedback Steering Wheel en-keyword=Rescue Robot kn-keyword=Rescue Robot en-keyword= Snake Robot. kn-keyword= Snake Robot. END start-ver=1.4 cd-journal=joma no-vol=3 cd-vols= no-issue= article-no= start-page=1120 end-page=1125 dt-received= dt-revised= dt-accepted= dt-pub-year=2003 dt-pub=20037 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=A study of reinforcement learning with knowledge sharing for distributed autonomous system en-subtitle= kn-subtitle= en-abstract= kn-abstract=Reinforcement learning is one of effective controller for autonomous robots. Because it does not need priori knowledge and behaviors to complete given tasks are obtained automatically be repeating trial and error. However a large number of trials are required to realize complex tasks. So the task that can be obtained using the real robot is restricted to simple ones. Considering these points, various methods that prove the learning cost of reinforcement learning have been proposed. In the method that uses priori knowledge, the methods lose the autonomy that is most important feature of reinforcement learning in applying it to the robots. In the Dyna-Q, that is one of simple and effective reinforcement learning architecture integrating online planning, a model of environment is learned from real experience and by utilizing the model to learn, the learning time is decreased. In this architecture, the autonomy is held, however the model depends on the task, so acquired knowledge of environment cannot be reused to other tasks. In the real world, human beings can learn various behaviors to complete complex tasks without priori knowledge of the tasks. We can try to realize the task in our image without moving our body. After the training in the image, by trying to the real environment, we save time to learn. It means that we have model of environment and we utilize the model to learn. We consider that the key ability that makes the learning process faster is construction of environment model and utilization of it. In this paper, we have proposed a method to obtain an environment model that is independent of the task. And by utilizing the model we have decreased learning time. We consider distributed autonomous agents, and we show that the environment model is constructed quickly by sharing the experience of each agent, even when each agent has own independent task. To demonstrate the effectiveness of the proposed method, we have applied the method to the Q-learning and simulations of a puddle world are carried out. As a result effective behaviors have been obtained quickly.
en-copyright= kn-copyright= en-aut-name=ItoKazuyuki en-aut-sei=Ito en-aut-mei=Kazuyuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=ImotoYoshiaki en-aut-sei=Imoto en-aut-mei=Yoshiaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=GofukuAkio en-aut-sei=Gofuku en-aut-mei=Akio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=TakeshitaMitsuo en-aut-sei=Takeshita en-aut-mei=Mitsuo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Okayama University affil-num=3 en-affil= kn-affil=Okayama University affil-num=4 en-affil= kn-affil=Okayama University en-keyword=knowledge based systems kn-keyword=knowledge based systems en-keyword= learning (artificial intelligence) kn-keyword= learning (artificial intelligence) en-keyword=planning (artificial intelligence) robots kn-keyword=planning (artificial intelligence) robots END start-ver=1.4 cd-journal=joma no-vol=3 cd-vols= no-issue= article-no= start-page=2500 end-page=2505 dt-received= dt-revised= dt-accepted= dt-pub-year=2003 dt-pub=200310 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Hybrid autonomous control for heterogeneous multi-agent system en-subtitle= kn-subtitle= en-abstract= kn-abstract=Reinforcement learning is an adaptive and flexible control method for autonomous system. In our previous works, we had proposed a reinforcement learning algorithm for redundant systems: "Q-learning with dynamic structuring of exploration space based on GA (QDSEGA)", and applied it to multi-agent systems. However previous works of the QDSEGA have been restricted to homogeneous agents. In this paper, we extend our previous works of multi-agent systems, and propose a hybrid autonomous control method for heterogeneous multi-agent systems. To demonstrate the effectiveness of the proposed method, simulations of transportation task by 10 heterogeneous mobile robots have been carried out. As a result effective behaviors have been obtained.
en-copyright= kn-copyright= en-aut-name=ItoKazuyuki en-aut-sei=Ito en-aut-mei=Kazuyuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=GofukuAkio en-aut-sei=Gofuku en-aut-mei=Akio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Okayama University en-keyword=adaptive control kn-keyword=adaptive control en-keyword= learning (artificial intelligence) kn-keyword= learning (artificial intelligence) en-keyword=mobile robots kn-keyword=mobile robots en-keyword=multi-agent systems kn-keyword=multi-agent systems END start-ver=1.4 cd-journal=joma no-vol= cd-vols= no-issue= article-no= start-page=239 end-page=244 dt-received= dt-revised= dt-accepted= dt-pub-year=2004 dt-pub=20048 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=A combined navigation strategy by a steering en-subtitle= kn-subtitle= en-abstract= kn-abstract=This paper applies our developed novice users oriented force feedback steering wheel interface and mouse interface to navigating a tank type rescue robot. By analyzing merits and limitation of operating each interface, we propose a combined navigation strategy by the two interfaces. The steering wheel interface consists of a force feedback steering control and a six monitors’ wall. Through this interface, users can navigate the tank robot like driving cars, while watching incoming videos. It provides a daily life operation method for novice users to navigate the tank rescue robot. The steering wheel interface is efficient in exploring open areas. For complex disaster fields, this interface requires users have skillful operation experiences, which take them more attention. The mouse-screen interface consists of a mouse and a camera’s view displayed in a computer screen. Through this interface, users can navigate the tank robot just by mouse clicking. Path planning and low-level controlling are realized by system automatically. The mouse-screen interface can realize exact navigation, especially needed in complex structures, without taking much attention. It gives users more time to care incoming information. The two interfaces can shift into each other at any time. The combined navigation strategy adopts merits of the two interfaces and compensates limitation of each of them. It provides an efficient operation method for novice users to navigate rescue robots.
en-copyright= kn-copyright= en-aut-name=YangZhixiao en-aut-sei=Yang en-aut-mei=Zhixiao kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=ItoKazuyuki en-aut-sei=Ito en-aut-mei=Kazuyuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=SaijoKazuhiko en-aut-sei=Saijo en-aut-mei=Kazuhiko kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=HirotsuneKazuyuki en-aut-sei=Hirotsune en-aut-mei=Kazuyuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=GofukuAkio en-aut-sei=Gofuku en-aut-mei=Akio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=MatsunoFumitoshi en-aut-sei=Matsuno en-aut-mei=Fumitoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Okayama University affil-num=3 en-affil= kn-affil=Okayama University affil-num=4 en-affil= kn-affil=Okayama University affil-num=5 en-affil= kn-affil=Okayama University affil-num=6 en-affil= kn-affil=University of Electro-Communications en-keyword=Human Interface; Rescue Robot; Navigation;Force Feedback Steering Wheel; Mouse; Tank Robot. kn-keyword=Human Interface; Rescue Robot; Navigation;Force Feedback Steering Wheel; Mouse; Tank Robot. END start-ver=1.4 cd-journal=joma no-vol= cd-vols= no-issue= article-no= start-page=239 end-page=244 dt-received= dt-revised= dt-accepted= dt-pub-year=2004 dt-pub=20048 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=A combined navigation strategy by a steering wheel and a mouse for a tank rescue robot en-subtitle= kn-subtitle= en-abstract= kn-abstract=This paper applies our developed novice users oriented force feedback steering wheel interface and mouse interface to navigating a tank type rescue robot. By analyzing merits and limitation of operating each interface, we propose a combined navigation strategy by the two interfaces. The steering wheel interface consists of a force feedback steering control and a six monitors’ wall. Through this interface, users can navigate the tank robot like driving cars, while watching incoming videos. It provides a daily life operation method for novice users to navigate the tank rescue robot. The steering wheel interface is efficient in exploring open areas. For complex disaster fields, this interface requires users have skillful operation experiences, which take them more attention. The mouse-screen interface consists of a mouse and a camera’s view displayed in a computer screen. Through this interface, users can navigate the tank robot just by mouse clicking. Path planning and low-level controlling are realized by system automatically. The mouse-screen interface can realize exact navigation, especially needed in complex structures, without taking much attention. It gives users more time to care incoming information. The two interfaces can shift into each other at any time. The combined navigation strategy adopts merits of the two interfaces and compensates limitation of each of them. It provides an efficient operation method for novice users to navigate rescue robots.
en-copyright= kn-copyright= en-aut-name=YangZhixiao en-aut-sei=Yang en-aut-mei=Zhixiao kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=ItoKazuyuki en-aut-sei=Ito en-aut-mei=Kazuyuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=SaijoKazuhiko en-aut-sei=Saijo en-aut-mei=Kazuhiko kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=HirotsuneKazuyuki en-aut-sei=Hirotsune en-aut-mei=Kazuyuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=GofukuAkio en-aut-sei=Gofuku en-aut-mei=Akio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=MatsunoFumitoshi en-aut-sei=Matsuno en-aut-mei=Fumitoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Okayama University affil-num=3 en-affil= kn-affil=Okayama University affil-num=4 en-affil= kn-affil=Okayama University affil-num=5 en-affil= kn-affil=Okayama University affil-num=6 en-affil= kn-affil=University of Electro-Communications en-keyword=Human Interface kn-keyword=Human Interface en-keyword=Rescue Robot kn-keyword=Rescue Robot en-keyword=Navigation kn-keyword=Navigation en-keyword=Force Feedback Steering Wheel kn-keyword=Force Feedback Steering Wheel en-keyword=Mouse kn-keyword=Mouse en-keyword= Tank Robot. kn-keyword= Tank Robot. END start-ver=1.4 cd-journal=joma no-vol=4 cd-vols= no-issue= article-no= start-page=2572 end-page=2579 dt-received= dt-revised= dt-accepted= dt-pub-year=2003 dt-pub=200312 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Emergence of adaptive behaviors by redundant robots : Robustness to changes environment and failures en-subtitle= kn-subtitle= en-abstract= kn-abstract=Acquiring adaptive behaviors of robots automatically is one of the most interesting topics of the evolutionary systems. In previous works, we have developed an adaptive autonomous control method for redundant robots. The QDSEGA is one of the methods that we have proposed for them. The QDSEGA is realized by combining Q-learning and GA, and it can acquire suitable behaviors by adapting a movement of a robot for a task. In this paper, we focus on the adaptability of the QDSEGA and discuss the robustness of the autonomous redundant robot that is controlled by the QDSEGA. To demonstrate the effectiveness of the QDSEGA, simulations of obstacle avoidance by a 10-link manipulator in the changeable environment and locomotion by a 12-legged robot with failures have been carried out, and as a result, adaptive behaviors for each environment and each broken body have emerged.
en-copyright= kn-copyright= en-aut-name=ItoKazuyuki en-aut-sei=Ito en-aut-mei=Kazuyuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=GofukuAkio en-aut-sei=Gofuku en-aut-mei=Akio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Okayama University en-keyword=genetic algorithms kn-keyword=genetic algorithms en-keyword= learning (artificial intelligence) kn-keyword= learning (artificial intelligence) en-keyword=legged locomotion kn-keyword=legged locomotion en-keyword=redundant manipulators kn-keyword=redundant manipulators END start-ver=1.4 cd-journal=joma no-vol=2 cd-vols= no-issue= article-no= start-page=723 end-page=728 dt-received= dt-revised= dt-accepted= dt-pub-year=1996 dt-pub=19963 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Development of a tactile sensing flexible actuator en-subtitle= kn-subtitle= en-abstract= kn-abstract=The disadvantages of flexible artificial fingers have been improved. The finger is provided with the tactile sense by two types of sensors to detect when the finger tip touches an object and to estimate both the finger force and object size. The rigidity is enhanced by equipping the finger with a reinforcing material similar to that of human bone. A prototype robot hand with four fingers has been manufactured for experiments and mounted on an industrial articulated robot. The effectiveness of the improved robot hand finger was confirmed throughout experimental tests of grasping action
en-copyright= kn-copyright= en-aut-name=TanakaYutaka en-aut-sei=Tanaka en-aut-mei=Yutaka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=GofukuAkio en-aut-sei=Gofuku en-aut-mei=Akio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=FujinoYuji en-aut-sei=Fujino en-aut-mei=Yuji kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= affil-num=1 en-affil= kn-affil=Department of Mecliariical Engineering, Faculty of Engineering, Okayama Univ. affil-num=2 en-affil= kn-affil=Department of Mecliariical Engineering, Faculty of Engineering, Okayama Univ. affil-num=3 en-affil= kn-affil=Murata Seisakusho en-keyword=actuators kn-keyword=actuators en-keyword=manipulators kn-keyword=manipulators en-keyword=tactile sensors kn-keyword=tactile sensors END start-ver=1.4 cd-journal=joma no-vol=3 cd-vols= no-issue= article-no= start-page=1827 end-page=1832 dt-received= dt-revised= dt-accepted= dt-pub-year=2000 dt-pub=200010 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Fundamental study of fluid transfer using electro-rheological effect en-subtitle= kn-subtitle= en-abstract= kn-abstract=Considering that there is no pump feeding an Electro-Rheological-Fluid, a new type of pump has been manufactured and fluid dynamic characteristics have been elucidated. This pump can feed the ERF by utilizing effectively the change in physical properties of the fluid by the application of voltage. The principle and configuration of this pump and the methods of theoretical analysis are described, and the influence of the voltage on the feeding characteristics was examined The dispersoidal ERF has been treated hitherto as a type of Newtonian fluid. However. experiments showed that not only the induced shear stress but also the viscosity is affected by the electric field strength and that the ERF must be treated as a pseudo-plastic flow. The method of analysis described here can be applied to design the pump differing in dimensions because the analysis gave qualitative evaluations about the flow rate and pressure difference
en-copyright= kn-copyright= en-aut-name=TanakaYutaka en-aut-sei=Tanaka en-aut-mei=Yutaka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=GofukuAkio en-aut-sei=Gofuku en-aut-mei=Akio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Okayama University en-keyword=electrorheology kn-keyword=electrorheology en-keyword=non-Newtonian flow kn-keyword=non-Newtonian flow en-keyword=non-Newtonian fluids kn-keyword=non-Newtonian fluids en-keyword=pumps kn-keyword=pumps END start-ver=1.4 cd-journal=joma no-vol= cd-vols= no-issue= article-no= start-page=932 end-page=937 dt-received= dt-revised= dt-accepted= dt-pub-year=1999 dt-pub=199909 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Development of a video-rate range finder using dynamic threshold method for characteristic point detection en-subtitle= kn-subtitle= en-abstract= kn-abstract=This study develops a video-rate stereo range finding circuit to obtain the depth of objects in a scene by processing video signals (R, G, B, and brightness signals) from binocular CCD cameras. The electronic circuit implements a dynamic threshold method to decrease the affect of signal noise in characteristic point detection, where a video signal from each CCD camera is compared with multiple thresholds, shifting dynamically by feeding back the previous comparison result. Several object depth measurement experiments for simple indoor scenes show that the dynamic threshold method gives high acquisition and correct rates of depth data compared with those by a fixed threshold method for the video signals and a relative method for R, G, and B signals utilized in the authors' previous range finders. en-copyright= kn-copyright= en-aut-name=TanakaYutaka en-aut-sei=Tanaka en-aut-mei=Yutaka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=GofukuAkio en-aut-sei=Gofuku en-aut-mei=Akio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=TakedaNobuo en-aut-sei=Takeda en-aut-mei=Nobuo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=NagaiIsaku en-aut-sei=Nagai en-aut-mei=Isaku kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= affil-num=1 en-affil= kn-affil=Department of Systems Engineering, Okayama University affil-num=2 en-affil= kn-affil=Department of Systems Engineering, Okayama University affil-num=3 en-affil= kn-affil=Graduate School of Engineering, Okayama University affil-num=4 en-affil= kn-affil=Department of Systems Engineering, Okayama University en-keyword=Video-Rate Range Finder kn-keyword=Video-Rate Range Finder en-keyword=Stereo Color CCD Camera kn-keyword=Stereo Color CCD Camera en-keyword=Autonomous Vehicle kn-keyword=Autonomous Vehicle en-keyword=Detection of Characteristic Point kn-keyword=Detection of Characteristic Point en-keyword=Real-Time Measurement kn-keyword=Real-Time Measurement END start-ver=1.4 cd-journal=joma no-vol= cd-vols= no-issue= article-no= start-page=67 end-page=72 dt-received= dt-revised= dt-accepted= dt-pub-year=1997 dt-pub=19970923 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Analysis of electric-fluid analogy of pressure transmission through an electro-rheological-fluid in annuli en-subtitle= kn-subtitle= en-abstract= kn-abstract=The article concerns the development of flexible robotic fingers using electro-rheological fluid (ERF) for pressure control. It describes a technique to predict the transient response of a pressure control device using ERF by an electric-flow analogy. The inertia is calculated from the theoretical equation. The resistance and additional voltage source by the ER effect are derived theoretically by assuming the flow in the electrode annuli of the pressure control device as a flow of the Bingham fluid. The capacitance is determined to compare the time-responses of pressures by the prediction based on a model with the results of a simple experiment. The predictions of transient flow, using the determined parameters of the model are in qualitatively good agreement with the experimental results
en-copyright= kn-copyright= en-aut-name=TanakaYutaka en-aut-sei=Tanaka en-aut-mei=Yutaka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=GofukuAkio en-aut-sei=Gofuku en-aut-mei=Akio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=NakamuraKeiji en-aut-sei=Nakamura en-aut-mei=Keiji kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Okayama University affil-num=3 en-affil= kn-affil=Okayama University en-keyword=electropneumatic control equipment kn-keyword=electropneumatic control equipment en-keyword=electrorheology kn-keyword=electrorheology en-keyword=manipulators kn-keyword=manipulators en-keyword=non-Newtonian flow kn-keyword=non-Newtonian flow en-keyword=non-Newtonian fluids kn-keyword=non-Newtonian fluids en-keyword=pressure control kn-keyword=pressure control END start-ver=1.4 cd-journal=joma no-vol= cd-vols= no-issue= article-no= start-page=1663 end-page=1668 dt-received= dt-revised= dt-accepted= dt-pub-year=2002 dt-pub=200210 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Motion planning for mobile manipulator with keeping manipulability en-subtitle= kn-subtitle= en-abstract= kn-abstract=Our research goal is to realize a motion planning for an intelligent mobile manipulator. To plan a mobile manipulator's motion, it is popular that the base robot motion is regarded as manipulator's extra joints, and the whole system is considered as a redundant manipulator. In this case, the locomotion controller is a part of the manipulator controller. However, it is difficult to implement both controllers as one controller, in our implementation experience, because of difference of actuators' character. In this research, we focus on a path planning algorithm for a mobile base with keeping manipulability at the tip of the mounted manipulator. In this case, the locomotion controller is independent from the manipulator controller, and a cooperative motion is realized by a communication between both controllers. In this paper, we propose a motion planning algorithm for a mobile manipulator, and report several experimental results.
en-copyright= kn-copyright= en-aut-name=NagataniKeiji en-aut-sei=Nagatani en-aut-mei=Keiji kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=HirayamaTomonobu en-aut-sei=Hirayama en-aut-mei=Tomonobu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=GofukuAkio en-aut-sei=Gofuku en-aut-mei=Akio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=TanakaYutaka en-aut-sei=Tanaka en-aut-mei=Yutaka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Okayama University affil-num=3 en-affil= kn-affil=Okayama University affil-num=4 en-affil= kn-affil=Okayama University en-keyword=manipulators kn-keyword=manipulators en-keyword=mobile robots kn-keyword=mobile robots en-keyword=path planning kn-keyword=path planning END