Path Planning for Robot in Multi-dimensional Environment Based on Dynamic PRM Blended Potential Field

Abstract

Probabilistic roadmap (PRM) can effectively solve the path planning problem in environment with high-dimension and complex constraints, but has limitations of low quality and efficiency of path planning in narrow channel and dynamic environment. In order to improve the applicability and dynamic characteristics of PRM, a path planning for mobile robot in Multi-dimensional environment based on dynamic PRM blended potential field is proposed in this paper. It contains three main parts: Firstly, the potential field is set in the workspace, then the number of adaptive sampling points and area-division are carried out according to the strength of the potential field. Secondly, the variable radius sampling strategy is designed for each area to make the distribution of sampling points more reasonable, after which the roadmap is built. Lastly, the changing potential field strategy is constructed to meet the change of the environment. Under the changing potential field force, the sampling point moves. The local reconstruction of the roadmap is carried out according to the change of the moving sampling points driven by the potential field force. By these strategies above, the connectivity and responsiveness of PRM in multi-dimensional environment is improved.