Sampling based mission planning for multiple robots

Abstract

Robots of the future would be sophisticated enough to do all kinds of tasks for the human master, while operating a team of robots would ensure that the tasks are done as efficiently as possible. This paper presents a solution to a very common mission wherein a team of robots needs to visit a number of mission sights and carry some operation there. The sights may have their own preference of robots. The real world workspace is first converted into a roadmap using the Probabilistic Roadmap algorithm. The adopted technique for roadmap generation uses a hybrid of narrow corridor sampler, obstacle based sampler and uniform sampler; with hybrid edge connection technique that aims to first connect disconnected roadmaps and then introduces redundant cycles and also ensures a good coverage of the configuration space. The roadmap is used to compute a cost matrix between every mission sight. This cost matrix is used by an optimization algorithm which deputes different mission sights to different robots as well as dictates the order of visit of the missions by each robot. Local optimization is used to quickly compute the optimal plan. The navigation of the robots is done using a Fuzzy Logic based navigator. Experiments show that the robots are able to coordinate with each other and complete the mission as a team very effectively.