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 differentiable 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.

This paper presents an integrated hardware-software solution to perform fully automated robotic bush trimming to user-specified shapes. In contrast to specialized solutions that can trim only bushes of a certain shape, the approach ensures flexibility via a vision-based shape fitting module that allows fitting an arbitrary mesh into a bush at hand. A trimming planning method considers the available degrees of freedom of the robot arm to achieve effective cutting motions. The performance of the mesh fitting module is assessed in multiple experiments involving both artificial and real plants with a variety of shapes. The trimming accuracy of the overall approach is quantitatively evaluated by inspecting the bush pointcloud before and after robotic trimming, and measuring the change in the deviation from the originally computed target mesh.

Time-optimal controls for 2-link robots are often of bang-bang type. Many algorithms to solve time-optimal robot control problems a-priori assume the optimal control to be bang-bang. Industrial robots very often have 5 or 6 links and then the associated time-optimal controls are usually singular. This paper presents a new algorithm that enables computation of both bang-bang and singular time-optimal controls for robots. The algorithm uses both the conjugate gradient and Gauss-Newton method to enhance its efficiency and does not require state-parameterization, which introduces additional errors. The algorithm is used to compute time-optimal controls for an industrial 5-link robot model including gravity and viscous friction.