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JRM Vol.28 No.1 pp. 17-30
doi: 10.20965/jrm.2016.p0017
(2016)

Paper:

Collision Avoidance Using Contact Information with Multiple Objects by Multi-Leg Robot

Tomohito Takubo*, Keishi Kominami**, Kenichi Ohara***, Yasushi Mae**, and Tatsuo Arai**

*Graduate School of Engineering, Osaka City University
3-3-138 Sugimoto, Sumiyoshi-ku, Osaka 558-8585, Japan

**Graduate School of Engineering Science, Osaka University
1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan

***Faculty of Science and Technology, Meijo University
1-501 Shiogamaguchi, Tempaku, Nagoya, Aichi 468-8502, Japan

Received:
March 4, 2015
Accepted:
October 9, 2015
Published:
February 20, 2016
Keywords:
multi-legged robot, hexapod robot, obstacle avoidance, motion planning
Abstract
In robotics, a walking through motion is complex because of the presence of multipoint contact objects in the working environment of a robot. To simplify the walking through motion of a robot, a virtual impedance field is implemented to the contact points of the robot and an object so that the robot avoids the object passively. The traveling direction of the robot is altered by a virtual repulsive force obtained from the position of the estimated obstacle and the virtual impedance field. The resulting action depends on the parameter of virtual impedance coefficients. Because a combination of parameters includes many things, reinforcement learning is employed to obtain an optimal motion. The optimization of the multipoint contact walking through motion of a robot is finally achieved by evaluating the walking motion while encountering complex obstacles in a dynamic simulator. The motion is implemented on a hexapod robot, and the results demonstrate the effectiveness of the proposed method.
Optimized multi-point contacting walking

Optimized multi-point contacting walking

Cite this article as:
T. Takubo, K. Kominami, K. Ohara, Y. Mae, and T. Arai, “Collision Avoidance Using Contact Information with Multiple Objects by Multi-Leg Robot,” J. Robot. Mechatron., Vol.28 No.1, pp. 17-30, 2016.
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