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Performance improvements of dual-arm cooperative harvesting robot

The growing area of kiwifruit in China has resulted in an annual increase in labor costs during the harvest period. In this paper, we present a dual-arm picking robot, which is specially designed for picking kiwifruit in trellis orchards to meet the needs of labor shortages. The performance improvement is reflected in the use of deep learning to identify on-site kiwifruit and formulate picking strategies to achieve the purpose of efficient picking. The harvesting robot consists of two robotic arms and a mobile platform. The end of the robotic arm is equipped with an end effector to ensure the safe picking of kiwifruit. The vision system consists of a deep learning system and an RGBD camera, and uses yoloV4 model to identify kiwifruit. The performance of the harvesting robot is evaluated by the test, and the results show that the harvesting success rate of the robot is 90%, and the target recognition rate is 95%.