Automating labor-intensive tasks such as crop monitoring with robots is essential for enhancing production and conserving resources. However, autonomously monitoring horticulture crops remains challenging due to their complex structures, which often result in fruit occlusions. Existing view planning methods attempt to reduce occlusions but either struggle to achieve adequate coverage or incur high robot motion costs. We introduce a global optimization approach for view motion planning that aims to minimize robot motion costs while maximizing fruit coverage. To this end, we leverage coverage constraints derived from the set covering problem (SCP) within a shortest Hamiltonian path problem (SHPP) formulation. While both SCP and SHPP are well-established, their tailored integration enables a unified framework that computes a global view path with minimized motion while ensuring full coverage of selected targets. Given the NP-hard nature of the problem, we employ a region-prior-based selection of coverage targets and a sparse graph structure to achieve effective optimization outcomes within a limited time. Experiments in simulation demonstrate that our method detects more fruits, enhances surface coverage, and achieves higher volume accuracy than the motion-efficient baseline with a moderate increase in motion cost, while significantly reducing motion costs compared to the coverage-focused baseline. Real-world experiments further confirm the practical applicability of our approach.

翻译：利用机器人自动化执行作物监测等劳动密集型任务对于提高产量和节约资源至关重要。然而，由于园艺作物结构复杂，常导致果实被遮挡，自主监测仍面临挑战。现有的视角规划方法试图减少遮挡，但要么难以实现充分的覆盖，要么导致机器人运动成本高昂。我们提出了一种用于视角运动规划的全局优化方法，旨在最小化机器人运动成本的同时最大化果实覆盖范围。为此，我们在最短哈密顿路径问题（SHPP）的框架内，利用了源自集合覆盖问题（SCP）的覆盖约束。尽管SCP和SHPP都是成熟问题，但它们的定制化整合形成了一个统一框架，能够计算出一条全局视角路径，在确保对选定目标完全覆盖的同时最小化运动量。鉴于该问题的NP难特性，我们采用了基于区域先验的覆盖目标选择和稀疏图结构，以在有限时间内获得有效的优化结果。仿真实验表明，与注重运动效率的基线方法相比，我们的方法能检测到更多果实，提高表面覆盖率，并获得更高的体积精度，同时运动成本仅适度增加；而与注重覆盖率的基线方法相比，则能显著降低运动成本。真实世界实验进一步证实了我们方法的实际适用性。