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：作物监测对于提高农业生产效率和效益至关重要。然而，监测甜椒植株等大型复杂结构面临重大挑战，尤其是果实频繁被遮挡的问题。传统的次优视点规划可能导致对作物的非结构化低效覆盖。为此，我们提出一种新型视点运动规划器，该方法构建包含可行视点姿态及邻近姿态间轨迹的图网络，从而考虑机器人运动约束。该规划器在图网络中搜索累计信息增益最高的视点序列，实现高效监测甜椒植株的同时最小化遮挡。生成的视点姿态旨在充分覆盖已检测果实并发现新果实。图及其对应的最优视点姿态序列在有限视界内计算，并随系统获取新信息以固定时间间隔自适应更新。我们通过搭载RGB-D相机的机械臂（固定于推车）进行仿真与真实实验验证该方法有效性。实验结果表明，相较于先进单次次优视点规划器，本规划器生成的视点姿态序列能够系统性地覆盖作物，在有限时间内实现更高的果实覆盖率。