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Field robotic harvesting is a promising technique in recent development ofagricultural industry. It is vital for robots to recognise and localise fruitsbefore the harvesting in natural orchards. However, the workspace of harvestingrobots in orchards is complex: many fruits are occluded by branches and leaves.It is important to estimate a proper grasping pose for each fruit beforeperforming the manipulation. In this study, a geometry-aware network, A3N, isproposed to perform end-to-end instance segmentation and grasping estimationusing both color and geometry sensory data from a RGB-D camera. Besides,workspace geometry modelling is applied to assist the robotic manipulation.Moreover, we implement a global-to-local scanning strategy, which enablesrobots to accurately recognise and retrieve fruits in field environments withtwo consumer-level RGB-D cameras. We also evaluate the accuracy and robustnessof proposed network comprehensively in experiments. The experimental resultsshow that A3N achieves 0.873 on instance segmentation accuracy, with an averagecomputation time of 35 ms. The average accuracy of grasping estimation is 0.61cm and 4.8$^{\circ}$ in centre and orientation, respectively. Overall, therobotic system that utilizes the global-to-local scanning and A3N, achievessuccess rate of harvesting ranging from 70\% - 85\% in field harvestingexperiments.

Geometry-Aware Fruit Grasping Estimation for Robotic Harvesting in Orchards