A performance index for planar repetitive workspace robots

When large surfaces need to be covered by a robotic system, the most common solution is to design or employ a robot with a comparably large workspace (WS), with high costs and high power requirements. In this paper, we propose a new methodology consisting in an efficient partitioning of the surface, in order to use robotic systems with a workspace of arbitrarily smaller size. These robots are called repetitive workspace robots (RWR). To support this method, we formally define a general index IRWR in order to evaluate the covering efficiency of the workspace. Three algorithms to compute the index are presented, the uniform expansion covering algorithm (UECA), the corrected inertial ellipsoid covering algorithm (CIECA), and the genetic covering algorithm (GCA). The GCA, which delivers a solution in the proximity of the global-best one, is used as a baseline for a comparison between the UECA and the CIECA. Eventually, we present the results of a performance analysis of the three algorithms in terms of computing time. The results show that the CIECA is the best algorithm for the evaluation of the IRWR, almost reaching the global-best solutions of the GCA. Finally, we illustrate a practical application with a comparison between two commercial industrial paint robots: the ABBTM IRB 550 and the CMAVR Robotics GR 6100.