Humanoid Arm Motion Planning Using Stereo Vision and RRT Search
Satoshi Kagami*,**, James J. Kuffner***,*, Koichi Nishiwaki****, Kei Okada****, Masayuki Inaba****, and Hirochika Inoue****
*Digital Human Lab., National Institute of Advanced Science and Technology, 2-41-6, Oumi, Kouto-ku, Tokyo, 135-0064 Japan
**CREST Program, JST (Japan Science and Technology Corporation)
***School of Computer Science, Carnegie Mellon University 5000, Forbes Ave., Pittsburgh, PA 15213-3891 USA
****Dept. of Mechano-Informatics, School of Information Science and Technology, Univ. of Tokyo 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656 Japan
This paper describes an experimental stereo vision based motion planning system for humanoid robots. The goal is to automatically generate arm trajectories that avoid obstacles in unknown environments from high-level task commands. Our system consists of three components: 1) environment sensing using stereo vision with disparity map generation and online consistency checking, 2) probabilistic mesh modeling in order to accumulate continuous vision input, and 3) motion planning for the robot arm using RRTs (Rapidly exploring Random Trees). We demonstrate results from experiments using an implementation designed for the humanoid robot H7.
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