Sampling-based safe path planning for robotic manipulators
A novel method to obtain safe paths for robotic manipulators is presented. It emulates the probabilistic roadmap planner by trying to establish a collision-free path that connects the start and the goal configuration via samples that belong to a quasi-random, low discrepancy sequence. The path construction is guided by a search algorithm with a heuristic function that includes a suitably tailored safety estimation. The measure of safety is based on the danger field - a recently proposed safety assessment for human-robot interaction. Thus, the planner provides not only collision-free paths, but strives for safer ones. In order to decrease the expected number of collision checks, we propose a novel method for testing whether local paths are collision-free or not. The method combines the standard binary collision checking with the concept of bubbles of free configuration space.