Robots are increasingly used in tomato greenhouses to automate labour-intensive tasks such as selective harvesting and de-leafing. To perform these tasks, robots must be able to accurately and efficiently perceive the plant nodes that need to be cut, despite the high levels of occlusion from other plant parts. We formulate this problem as a local next-best-view (NBV) planning task where the robot has to plan an efficient set of camera viewpoints to overcome occlusion and improve the quality of perception. Our formulation focuses on quickly improving the perception accuracy of a single target node to maximise its chances of being cut. Previous methods of NBV planning mostly focused on global view planning and used random sampling of candidate viewpoints for exploration, which could suffer from high computational costs, ineffective view selection due to poor candidates, or non-smooth trajectories due to inefficient sampling. We propose a gradient-based NBV planner using differential ray sampling, which directly estimates the local gradient direction for viewpoint planning to overcome occlusion and improve perception. Through simulation experiments, we showed that our planner can handle occlusions and improve the 3D reconstruction and position estimation of nodes equally well as a sampling-based NBV planner, while taking ten times less computation and generating 28% more efficient trajectories.

翻译：机器人越来越多地被用于番茄温室中，以自动化劳动密集型任务，例如选择性采摘和去叶。为了执行这些任务，机器人必须能够准确且高效地感知需要切割的植物节点，尽管存在来自其他植物部件的高度遮挡。我们将此问题表述为局部最优视角（NBV）规划任务，其中机器人需要规划一组高效的相机视角以克服遮挡并提高感知质量。我们的方法专注于快速提高单个目标节点的感知精度，以最大化其被切割的机会。以往的NBV规划方法主要关注全局视角规划，并使用随机采样候选视角进行探索，这可能导致高计算成本、因候选视角不佳而导致的有效视角选择困难，或因采样效率低下导致的非平滑轨迹。我们提出了一种基于梯度的NBV规划器，采用微分射线采样，直接估计用于视角规划的局部梯度方向，以克服遮挡并提高感知。通过仿真实验，我们展示了我们的规划器在处理遮挡以及提高节点三维重建和位置估计方面与基于采样的NBV规划器表现相当，同时计算量减少了十倍，并生成了效率高出28%的轨迹。