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.

翻译：移动机械臂在执行操作任务前常需进行近距离观测。我们将此问题命名为耦合主动感知与操作（CAPM）问题，对感知过程中的不确定性进行建模，并提出一种考虑可达性条件作为移动平台感知及操作任务约束的关键状态/任务规划方法。通过在满足任务约束的前提下最小化机体关键状态规划中的预期能量消耗，本算法实现了任务成功率与能量消耗之间的最佳平衡。我们已在仿真与物理实验中实现并测试该算法。结果表明，与两阶段解耦方法相比，本算法在保持100%任务成功率的同时，具有更低的能耗。