Efficient Path Planning for Modular Reconfigurable Robots

Industrial robots are essential for modern production but often struggle to adapt to new tasks. Modular (reconfigurable) robots can overcome this challenge by eliminating 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%.