Reactive Constrained Model Predictive Control for Redundant Mobile Manipulators

Research interest in redundant mobile manipulators has been constantly increasing during the last decade. The opportunities offered by the redundant degrees of freedom, together with the exploitation of the mobile base, would allow such robots to complete their main task while complying with additional tasks or constraints. These features would make it easier for robots to work in a partly unstructured and dynamic environment, thus increasing production flexibility. In this work, a reactive constraint-based control strategy for mobile manipulators is proposed, which accomplishes a positioning task while simultaneously avoiding unknown and unpredictable obstacles. Differently from other approaches, the trajectory is computed exclusively online, by exploiting the MPC method, without the need of a pre-planned path. Experimental verification on a KUKA youBot shows the applicability of the approach.