Parameter Optimization in Non-uniform Randomized Footstep Planning for Biped Navigation

In our previous work, a random sampling-based footstep planner has been proposed for global biped navigation in environments with local minima or narrow passages. Goal-Probability Threshold (GPT) is a key parameter which controls the convergence rate of the goal-biased non-uniform sampling in this planner. In this paper, a GPT optimization approach is explained. We construct a benchmarking model, in which the biped navigation problem is described with selected parameters. We study the relationship between these parameters and the optimized GPT and employ a back propagation (BP) neural network to fit this relationship. With a trained BP neural network modular, an optimized GPT can be automatically generated according to the planning problem specifications. Compared to previous methods of manual and empirical tuning of GPT for individual planning problem, the proposed approach is a self-adaptive approach. Numerical experiments verified the quite satisfying performance of the proposed approach, and also showed that the footstep planner performs much stable with BP-generated GPTs.