精准农业应用中移动机械臂的耦合主动感知与操作规划
Coupled Active Perception and Manipulation Planning for a Mobile Manipulator in Precision Agriculture Applications
AI总结：
针对精准农业中移动机械臂需先近距感知再操作的CAPM问题，提出考虑可达性约束并最小化预期能耗的关键状态/任务规划方法，仿真与实物实验表明其较两阶段解耦方法更节能且任务成功率达100%。
摘要：
移动机械臂经常处于这样的应用场景：在执行操作任务之前，需要先进行近距离观察。我们将此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，并设计了一种关键状态/任务规划方法，该方法将可达性条件视为移动平台感知任务和操作任务的任务约束。通过在身体关键状态规划中最小化预期能耗，同时满足任务约束，我们的算法在任务成功率和能耗之间实现了最佳平衡。我们已实现该算法，并在仿真和物理实验中对其进行了测试。结果证实，与两阶段解耦方法相比，我们的算法能耗更低，同时仍保持100%的任务成功率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
