Coupled Active Perception and Manipulation Planning for a Mobile Manipulator in Precision Agriculture Applications

A mobile manipulator often finds itself in an application where it needs to take a close-up view before performing a manipulation task. Named this as a coupled active perception and manipulation (CAPM) problem, we model the uncertainty in the perception process and devise a key state/task planning algorithm that considers reachability condi- tions jointly established from perception and manipulation task constraints. By minimizing expected energy usage in body key state planning while satisfying task constraints, our algorithm is able to find an energy-efficient trajectory with less body repositioning motion while ensuring the success of the task. We have implemented the algorithm and tested it in both simulation and physical experiments. The results have confirmed that our algorithm has a lower energy consumption compared to a two- stage decoupled approach, while still maintaining a success rate of 100% for the task.