Mobile Manipulator Planning under Uncertainty in Unknown Environments

We present a sampling-based mobile manipulator planner that considers the base pose uncertainty and the effects of this uncertainty on manipulator motions. The overall planner has three distinct and novel features: i) it uses the Hierarchical and Adaptive Mobile Manipulator Planner (HAMP) that plans for both the base and the arm in a judicious manner, ii) it uses localization aware sampling and connection strategies to consider only those nodes and edges which contribute toward better localization, iii) it incorporates base pose uncertainty along the edges (where arm remains static) and the effects of this uncertainty are considered on arm motion. We call this overall planner HAMP-BUA, where BUA stands for Base pose Uncertainty and its propagation to Arm motions. First we evaluate our planner in known static environments and show that it finds a safer path as compared to other variants where uncertainty is not considered at different levels as mentioned above. Next, we incorporate our planner within an integrated and fully autonomous system for mobile pick-and-place tasks in unknown static environments. A key aspect of our integrated system is that the planner works in tandem with base and arm exploration modules that explore the unknown environment. Our system is implemented both in simulation and on the actual SFU mobile manipulator and we present the corresponding results. It demonstrates a level of competency in exploring unknown environments for carrying out pick-and-place tasks that has not been demonstrated before.