Inverse Reachability Map Guided Motion Planning of Mobile Manipulator

Abstract

Mobile manipulators must coordinate end-effector (EE) tracking and mobile base motion to perform manipulation tasks robustly. However, even when the same EE trajectory is feasible, different base poses can lead to substantially different manipulator configurations, manipulability levels, and proximity to singularities. Thus, accurate EE tracking does not guarantee kinematically suitable whole-body behavior. To address this issue, a hierarchical framework is proposed that combines 1) an manipulator controller for EE tracking considering base motion, 2) an inverse reachability map (IRM) that encodes kinematically feasible base regions for the current and predicted EE states, and 3) a model predictive controller (MPC) that optimizes base velocity using the IRM as a soft cost. In the proposed architecture, the manipulator executes the task, the IRM evaluates which base regions are more reachable for the task, and the