Crop monitoring is crucial for maximizing agricultural productivity and efficiency. However, monitoring large and complex structures such as sweet pepper plants presents significant challenges, especially due to frequent occlusions of the fruits. Traditional next-best view planning can lead to unstructured and inefficient coverage of the crops. To address this, we propose a novel view motion planner that builds a graph network of viable view poses and trajectories between nearby poses, thereby considering robot motion constraints. The planner searches the graphs for view sequences with the highest accumulated information gain, allowing for efficient pepper plant monitoring while minimizing occlusions. The generated view poses aim at both sufficiently covering already detected and discovering new fruits. The graph and the corresponding best view pose sequence are computed with a limited horizon and are adaptively updated in fixed time intervals as the system gathers new information. We demonstrate the effectiveness of our approach through simulated and real-world experiments using a robotic arm equipped with an RGB-D camera and mounted on a trolley. As the experimental results show, our planner produces view pose sequences to systematically cover the crops and leads to increased fruit coverage when given a limited time in comparison to a state-of-the-art single next-best view planner.

翻译：作物监测对于最大化农业生产力和效率至关重要。然而，监测甜椒植株等大型复杂结构存在显著挑战，尤其是果实频繁被遮挡。传统的次优视角规划可能导致作物覆盖无结构化且效率低下。为解决这一问题，我们提出了一种新颖的视角运动规划器，该方法构建了可行视角位姿及邻近位姿间轨迹的图网络，从而考虑机器人运动约束。该规划器在图网络中搜索累积信息增益最高的视角序列，实现高效监测甜椒植株的同时最小化遮挡。生成的视角位姿旨在充分覆盖已检测到的果实，并发现新果实。图及其对应的最优视角位姿序列通过有限时域计算，并随着系统获取新信息而在固定时间间隔内自适应更新。我们通过使用配备RGB-D相机并安装在推车上的机械臂进行仿真和真实世界实验，验证了方法的有效性。实验结果表明，与现有单次最优视角规划器相比，我们的规划器生成的视角位姿序列能够系统地覆盖作物，并在有限时间内提高果实覆盖率。