Path Planning for a Statically Stable Biped Robot Using PRM and Reinforcement Learning

Abstract

In this paper path planning and obstacle avoidance for a statically stable biped robot using PRM and reinforcement learning is discussed. The main objective of the paper is to compare these two methods of path planning for applications involving a biped robot. The statically stable biped robot under consideration is a 4-degree of freedom walking robot that can follow any given trajectory on flat ground and has a fixed step length of 200 mm. It is proved that the path generated by the first method produces the shortest smooth path but it also increases the computational burden on the controller, as the robot has to turn at almost all steps. However the second method produces paths that are composed of straightline segments and hence requires less computation for trajectory following. Experiments were also conducted to prove the effectiveness of the reinforcement learning based path planning method.

Comparative research on path planning algorithms for autonomous mobile robots based on ROS

“…Where G(s) represents the set of all states that can be reached from the starting point s, N(s) represents the set of all states adjacent to state s, U represents the union, and G(s') represents the set of all states that can be reached from state s' [8].…”

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“…Probability sampling-based algorithms, such as the probabilistic roadmap method (PRM) [109] and the rapidly exploring random tree (RRT) [110], show superiority in their theoretical properties (in terms of probability integrity or asymptotic optimality), which renders them among successful methods for AUV path search. Note that the premise of completing the sampling algorithm is to have the corresponding environmental information of the operating area.…”

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