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

TL;DR

This paper introduces a coupled active perception and manipulation planning method for mobile manipulators in precision agriculture, optimizing energy use while ensuring task success through integrated planning.

Contribution

It models perception uncertainty and develops a key state planning algorithm that balances energy efficiency and task constraints in coupled perception and manipulation tasks.

Findings

Lower energy consumption compared to decoupled approaches

Achieves 100% task success rate in experiments

Validated in both simulation and physical tests

Abstract

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 approach that considers reachability conditions as task constraints of both perception and manipulation tasks for the mobile platform. By minimizing the expected energy usage in the body key state planning while satisfying task constraints, our algorithm achieves the best balance between the task success rate and energy usage. 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.