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Collision-free motion planning of a virtual arm based on the FABRIK algorithm

Published online by Cambridge University Press:  18 April 2016

Songqiao Tao and
Yumeng Yang
Songqiao Tao*
Affiliation:
Department of Mechanic and Electronic Engineering, Wuhan Technical College of Communications, 6 Baishazhou Road, Wuhan City, Hubei Province, 430065, P. R. China
Yumeng Yang
Affiliation:
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan City, Hubei Province, 430074, P. R. China Email: yym20071202@163.com
*
*Corresponding author. E-mail: taosongqiao@163.com
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Summary

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Collision-free motion planning of a virtual arm is an intractable task in high-interference environments. In this paper, an approach for collision-free motion planning of a virtual arm based on the forward and backward reaching inverse kinematics (FABRIK) algorithm is proposed. First, a random rotation strategy and local optimum-seeking technology are introduced to improve the FABRIK algorithm in order to avoid obstacles. The improvement FABRIK algorithm is used to design the final grasping posture of a virtual arm according the target position. Then, a bidirectional rapidly exploring random tree (Bi-RRT) algorithm is adopted to explore the process postures from a given initial posture to the final grasping posture. Different from the existing method, the proposed Bi-RRT algorithm in this paper plans the motions of a virtual arm in a seven-dimensional angle space, and the final grasping posture is automatically designed using the obstacle-avoidance FABRIK algorithm instead of the manual design. Finally, the post-processing technique is introduced to remove redundant nodes from the planned motions. This procedure has resolved the problem that the Bi-RRT algorithm is a random algorithm. The experimental results show the proposed method for collision-free motion planning of a virtual arm is feasible.

Keywords

Collision-freeMotion planningInverse kinematicsObstacle avoidanceVirtual arm

Information

Type
Articles
Information
Robotica , Volume 35 , Issue 6 , June 2017, pp. 1431 - 1450
Copyright
Copyright © Cambridge University Press 2016 

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