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Parameter self-adaptation in biped navigation employing nonuniform randomized footstep planner

Published online by Cambridge University Press:  15 January 2010

Zeyang Xia ,
Jing Xiong  and
Ken Chen
Zeyang Xia*
Affiliation:
Department of Precision Instruments and Mechanology, Tsinghua University, Beijing 100084, China. Mechanical and Aerospace Engineering, Nanyang Technological University, 639798Singapore.
Jing Xiong
Affiliation:
Department of Precision Instruments and Mechanology, Tsinghua University, Beijing 100084, China.
Ken Chen
Affiliation:
Department of Precision Instruments and Mechanology, Tsinghua University, Beijing 100084, China. State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China.
*
*Corresponding author. E-mail: [email protected]
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Summary

In our previous work, a random-sampling-based footstep planner has been proposed for global biped navigation. Goal-probability threshold (GPT) is the key parameter that controls the convergence rate of the goal-biased nonuniform sampling in the planner. In this paper, an approach to optimized GPT adaptation is explained by a benchmarking planning problem. We first construct a benchmarking model, in which the biped navigation problem is described in selected parameters, to study the relationship between these parameters and the optimized GPT. Then, a back-propagation (BP) neural network is employed to fit this relationship. With a trained BP neural network modular, the optimized GPT can be automatically generated according to the specifications of a planning problem. Compared with previous methods of manual and empirical tuning of GPT for individual planning problems, the proposed approach is self-adaptive. Numerical experiments verified the performance of the proposed approach and furthermore showed that planning with BP-generated GPTs is more stable. Besides the implementation in specific parameterized environments studied in this paper, we attempt to provide the frame of the proposed approach as a reference for footstep planning in other environments.

Keywords

Biped navigationRandomized samplingParameter self-adaptationRapidly exploring random treesNeural network
Type
Article
Information
Robotica , Volume 28 , Issue 6 , October 2010 , pp. 929 - 936
Copyright
Copyright © Cambridge University Press 2010

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