Efficient Path Planning for Modular Reconfigurable Robots

Industrial robots are essential for modern pro- duction but often struggle to adapt to new tasks. Modular (reconfigurable) robots can overcome this challenge by elim- inating the need to replace the whole robot. However, finding the optimal assembly for a task remains difficult because a valid path has to be computed for each generated assembly – consuming a significant fraction of the computation time. Similar to online path planning, where previous approaches adapt known paths to a changing environment, we show that transferring paths from previously considered module assemblies accelerates path planning for the next assemblies. On average, our method reduces the planning time for single- goal tasks by 50 %. The usefulness of our method is evaluated by integrating it in a genetic algorithm (GA) for optimizing assemblies and evaluating it on our benchmark suite CoBRA. Within the optimization loop for modular robots, the time used to check a single assembly is shortened by up to 50 %.