Completely Randomized RRT-Connect: A Case Study on 3D Rigid Body Motion Planning

Nowadays sampling-based motion planners use the power of randomization to compute multidimensional motions at high performance. Nevertheless the performance is based on problem-dependent parameters like the weighting of translation versus rotation and the planning range of the algorithm. Former work uses constant user-adjusted values for these parameters which are defined a priori. Our new approach extends the power of randomization by varying the parameters randomly during runtime. This avoids a preprocessing step to adjust parameters and moreover improves the performance in comparison to existing methods in the majority of the benchmarks. Our method is simple to understand and implement. In order to compare our approach we present a comprehensive experimental analysis about the parameters and the resulting performance. The algorithms and data structures were implemented in our own library RASAND, but we also compare the results of our work with OMPL [12] and the commercial software KineoTM Kite Lab [15].