Design of and Experiment with a Dual-Arm Apple Harvesting Robot System

Robotic harvesting has become an urgent need for the development of the apple industry, due to the sharp decline in agricultural labor. At present, harvesting apples using robots in unstructured orchard environments remains a significant challenge. This paper focuses on addressing the challenges of perception, localization, and dual-arm coordination in harvesting robots and presents a dual-arm apple harvesting robot system. First, the paper introduces the integration of the robot's hardware and software systems, as well as the control system architecture, and describes the robot's workflow. Secondly, combining a dual-vision perception system, the paper adopts a fruit recognition method based on a multi-task network model and a frustum-based fruit localization approach to identify and localize fruits. Finally, to improve collaboration efficiency, a multi-arm task planning method based on a genetic algorithm is used to optimize the target harvesting sequence for each arm. Field experiments were conducted in an orchard to evaluate the overall performance of the robot system. The field trials demonstrated that the robot system achieved an overall harvest success rate of 76.97%, with an average fruit picking time of 7.29 s per fruit and a fruit damage rate of only 5.56%.