Parallelized Sampling-based Path Planning for Tree-structured Rigid Robots

Sampling-based algorithms have become the favored approach for solving path and motion planning problems due to their ability to successfully deal with complex high-dimensional planning scenarios. This thesis presents an overview of existing sampling-based path planning methods for tree-structured rigid robots. The two most prominent algorithm families, Rapidly-exploring Dense Trees and Probabilistic Roadmaps, are examined with respect to their computational parallelizability. In addition, a parallel cell-based roadmap planning algorithm is proposed, which utilizes a novel dimensionality reduction technique for configuration space grids. The described methods are benchmarked on a number of 2D scenarios using a newly developed path planning library. The results show that on average significant speedups can be achieved, but that the individual algorithms scale very differently. Critical points in the current implementation are discussed and future improvements are suggested.