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:作物监测对于实现农业生产力和效率最大化至关重要。然而,监测甜辣椒植物等大型和复杂结构带来了重大挑战,特别是因为水果经常被封闭。传统的次最佳视图规划可能导致作物覆盖无结构且效率低下。为了解决这个问题,我们提议了一个新型的视觉运动规划器,以建立一个由附近形状和轨道构成的可行视图组成的图形网络,从而考虑到机器人运动的限制。规划器在搜索图表时,以累积的信息收益最高的方式查看顺序,允许高效的辣椒植物监测,同时尽量减少封闭性。生成的视图旨在充分覆盖已经检测到的和发现的新水果。图表和相应的最佳视图情景排列顺序可以在有限的范围内进行计算,并在系统收集新信息时以固定的时间间隔进行适应性更新。我们通过使用装有RGB-D相机的机器人模拟和真实世界实验,并在推土机上安装。实验结果显示,我们的规划器生成了系统覆盖作物的顺序,并在与一州最佳计划相比时间有限的情况下导致扩大水果覆盖范围。</s>