A Dynamic Sampling-Based Motion Planner for Humanoid Robots
Mohammed Mahfouz Elmogy · SSRN Electronic Journal · 2011
The autonomous navigation for humanoid robots comprises an increasingly important research area. The development of practical motion planning algorithms and obstacle avoidance techniques is considered as one of the most important fields of study in the task of building autonomous or semi-autonomous robot systems. In this paper, we present a time-efficient hybrid motion planning system for a humanoid robot in indoor and miniature city environments. The proposed technique is a combination of sampling-based planner and D* Lite search to generate dynamic footstep placements in unknown environments. It generates the search space depending on non-uniform sampling of the free configuration space to direct the computational resources to troubled and difficult. A modified cylinder model is used to approximate the trajectory for the robot's body-center during navigation. It calculates the actual distances required to execute different actions of the robot and compare them to the distances from the nearest obstacles. D* Lite search is then implemented to find dynamic and low-cost footstep placements within the resulting configuration space. The proposed hybrid algorithm reduces the searching time and produces a smoother path for the humanoid robot with low cost.