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.

机器人在番茄大棚中越来越多地用于自动化劳动密集型任务，但面临着植物其他部分的遮挡引起的感知困难。我们提出了一种基于梯度的下一最佳观测点规划方法，通过差分射线采样直接估计视点规划的局部梯度方向，旨在克服遮挡问题并提高感知质量，在模拟实验中证明了该方法的有效性。