A hierarchical and adaptive mobile manipulator planner with base pose uncertainty

We present a Hierarchical and Adaptive Mobile Manipulator Planner (HAMP) that plans for both the base and the arm in a judicious manner allowing the manipulator to change its configuration autonomously when needed if the current arm configuration is in collision with the environment as the mobile manipulator moves along the planned path. This is in contrast to current implemented approaches that are conservative and fold the arm into a fixed home configuration. Our planner first constructs a base roadmap and then for each node in the roadmap it checks for collision status of current manipulator configuration along the edges formed with adjacent nodes, if the current manipulator configuration is in collision, the manipulator C-space is searched for a new reachable configuration such that it is collision-free as the mobile manipulator moves along the edge and a path from current configuration to the new reachable configuration is computed. We show that HAMP is probabilistically complete. We compared HAMP with full 9D PRM and observed that the full 9D PRM is outperformed by HAMP in each of the performance criteria, i.e., computational time, percentage of successful attempts, base path length, and most importantly, undesired motions of the arm. We also evaluated the tree versions of HAMP, with RRT and Bi-directional RRT as core underlying subplanners, and observed similar advantages, although the time saving for Bi-directional RRT version is modest. We then present an extension of HAMP (we call it Vinay Pilania · Kamal Gupta Robotic Algorithms & Motion Planning (RAMP) Lab School of Engineering Science, Simon Fraser University Burnaby, BC V5A1S6, Canada E-mail: [email protected] Kamal Gupta E-mail: [email protected] HAMP-U) that uses belief space planning to account for localization uncertainty associated with the mobile base position and ensures that the resultant path for the mobile manipulator has low uncertainty at the goal. Our experimental results show that the paths generated by HAMP-U are less likely to result in collision and are safer to execute than those generated by HAMP (without incorporating uncertainty), thereby showing the importance of incorporating base pose uncertainty in our overall HAMP algorithm.