Abstract:

[Objective] During field harvesting operations, dual-arm robots often face challenges such as insufficient workspace coverage of the dual arms, substantial overlap between collaborative working areas, and fixed-step movement patterns that are poorly adapted to spatial variations in cluster distribution. These issues can lead to excessive workspace overlap, between the two manipulators, low workspace utilization, and missed harvests due to rigid step sizes, thereby limiting the robot's continuous operation capability and overall harvesting efficiency. To address these problems, the optimization of hand-eye configuration and an adaptive chassis movement strategy for dual-arm picking robots are investigated in this study, aiming to improve workspace utilization and enhance continuous operation performance and overall harvesting efficiency. [Methods] A dual-arm grape picking robot equipped with an onboard storage and buffering system was designed. The robot consisted of a mobile chassis, a dual-arm picking system, a vision perception system, a storage and buffering system, and a control system. Based on operational requirements of the dual-arm collaborative picking system, an effective workspace model was established by intersecting the reachable workspaces of the robotic arms, the depth camera's field of view, and the grape cluster distribution zones. A hand-eye layout optimization model was developed by considering occlusion relationships, using camera row spacing and distance from the camera to the trellis wall as optimization variables. Optimal layout parameters were determined through a two-parameter uniform grid search. Sensitivity analysis was performed for workspace utilization and workspace overlap under different layout configurations, revealing how layout parameters affected workspace coverage and dual-arm picking performance, thereby enabling quantitative optimization rather than empirical parameter selection. To address difficulties in detecting small peduncles and acquiring accurate depth information under long-range "eye-to-hand" visual configurations, the vision system combined YOLOv8 object detection with geometric modeling of clusters to detect grape clusters and estimate peduncle cutting points. Target tracking was further employed to associate clusters with their respective cutting points, providing reliable target coordinates for robotic picking. Additionally, an adaptive chassis movement strategy based on the spatial distribution of peduncle cutting points was proposed to reduce missed harvests caused by fixed-step motion. This strategy dynamically switched between forward and compensatory steps according to whether cutting points fell within uncovered regions of the manipulator workspace, helping to ensure continuous coverage of the picking area and reduce missed clusters due to uneven distribution. Field trials were conducted across five scenarios with different cluster distributions in a standardized horizontal trellis vineyard, comparing the adaptive movement strategy against the fixed-step approach. [Results and Discussions] The optimized hand-eye layout achieved a workspace utilization rate of 89.7%, while the workspace overlap between the two manipulators was reduced to 3.5%, thereby improving coverage of the grape-cluster harvesting region. Sensitivity analysis further revealed that both camera row spacing and distance from the camera to the trellis wall markedly influenced workspace utilization and arm overlap; the optimal parameter settings balanced workspace coverage and dual-arm collaboration performance. Vision experiments reached 100% cluster detection accuracy and 96% cutting-point localization accuracy, meeting the positioning requirements of the robot. Field picking tests demonstrated that the robot achieved an average picking success rate of 94%, representing an 11% improvement over the fixed-step strategy and a reduction of approximately 12% in the missed-harvest rate. [Conclusions] The proposed robot enables stable and continuous field picking operations. The proposed hand-eye layout optimization method improves workspace utilization. The proposed adaptive chassis motion strategy reduces the missed-harvest rate and improves the picking success rate and operational stability. The research results provide a theoretical basis and engineering reference for optimizing the hand-eye system layout, designing chassis motion strategies, and achieving continuous and efficient grape picking in horizontal trellis systems.

Key words: grape picking robot, hand-eye configuration, motion planning, adaptive chassis, dual-arm robot