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This paper presents an optimization-based solution to task and motionplanning (TAMP) on mobile manipulators. Logic-geometric programming (LGP) hasshown promising capabilities for optimally dealing with hybrid TAMP problemsthat involve abstract and geometric constraints. However, LGP does not scalewell to high-dimensional systems (e.g. mobile manipulators) and can suffer fromobstacle avoidance issues. In this work, we extend LGP with a sampling-basedreachability graph to enable solving optimal TAMP on high-DoF mobilemanipulators. The proposed reachability graph can incorporate environmentalinformation (obstacles) to provide the planner with sufficient geometricconstraints. This reachability-aware heuristic efficiently prunes infeasiblesequences of actions in the continuous domain, hence, it reduces replanning bysecuring feasibility at the final full trajectory optimization. Our frameworkproves to be time-efficient in computing optimal and collision-free solutions,while outperforming the current state of the art on metrics of success rate,planning time, path length and number of steps. We validate our framework onthe physical Toyota HSR robot and report comparisons on a series of mobilemanipulation tasks of increasing difficulty.

R-LGP: A Reachability-guided Logic-geometric Programming Framework for Optimal Task and Motion Planning on Mobile Manipulators