Inverse Reachability Map Guided Motion Planning of Mobile Manipulator
JungHyun Choi, taegyeom Lee, Myun Joong Hwang
AI summary
Problem
Mobile manipulators often track end-effector paths accurately but adopt kinematically poor base poses that reduce dexterity and increase singularity risk. Existing controllers lack explicit encoding of kinematically feasible base regions for the manipulator.
Approach
A hierarchical framework integrates an end-effector tracking controller with an Inverse Reachability Map (IRM) that scores feasible base regions, feeding this data as a soft cost into a Model Predictive Controller to optimize base velocity.
Key results
- Reduced end-effector tracking error compared to OCS2 baseline
- Preserved high manipulator manipulability during task execution
- Maintained kinematically favorable arm configurations away from singularities
- Successful execution of spray-painting and inspection tasks via IRM-guided base placement
Why it matters
Enables mobile manipulators to dynamically position their base for optimal dexterity, improving robustness in complex manipulation tasks.
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 MPC generates base motion accordingly. Simulation results demonstrate that the proposed method improves manipulability while maintaining accurate EE tracking, highlighting the importance of reachability-aware base behavior in mobile manipulation.