Kleiner, Alexander Sun, D. Meyer-Delius, D. 2011 (English)In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), IEEE conference proceedings, 2011, 3276-3282Conference paper (Refereed)
Autonomous robot teams that simultaneously dispatch transportation tasks are playing more and more an important role in present logistic centers and manufacturing plants. In this paper we consider the problem of robot motion planning for large robot teams in the industrial domain. We present adaptive road map optimization (ARMO) that is capable of adapting the road map in real time whenever the environment has changed. Based on linear programming, ARMO computes an optimal road map according to current environmental constraints (including human whereabouts) and the current demand for transportation tasks from loading stations in the plant. For detecting dynamic changes, the environment is describe by a grid map augmented with a hidden Markov model (HMM). We show experimentally that ARMO outperforms decoupled planning in terms of computation time and time needed for task completion.
Identifiersurn:nbn:se:liu:diva-72519 (URN)10.1109/IROS.2011.6048339 (DOI)oai:DiVA.org:liu-72519 (OAI)diva2:459928 (DiVA)
IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)
ProjectsArtificial Intelligence & Integrated Computer Systems