Design, Development, Integration and Field Evaluation of a Dual Robotic Arm Mango Harvesting Robot

Abstract

To solve the problems of high labor intensity and high cost when picking mango manually, a mango picking robot system with dual robotic arms was developed to realize automatic mango picking. Firstly, the YOLOMS network was used to realize the 3D localization of picking points for single mangoes and mango clusters in unstructured environments. Secondly, a new “shearing and grasping integrated” end-effector for non-destructive harvesting of mangoes was designed. Then, a task division method for the workspace of the dual robotic arm harvesting robot was proposed to minimize the likelihood of collisions between dual arms. Additionally, a depth-first picking strategy was introduced to reduce fruit damage and enhance the success rates of picking mangoes from layered canopies. Finally, a mango harvesting robotic system with dual arms was developed and integrated. The performance of the system was evaluated by field mango picking experiments. The results showed that the average recognition rate and planning success rate of the harvesting robot were 83.94% and 98.45%, respectively. In addition, the average harvesting success rate of the robot was 73.92%, and the average single-fruit harvesting time was 8.93 s. Compared with the robot with single arm, the harvesting time was reduced by 48.38%, which indicated that the harvesting efficiency of the dual robotic arm harvesting robot was significantly improved. The average collision-free harvesting rate with the addition of the depth-first harvesting strategy was 91.68%, which verified the rationality and effectiveness of the dual robotic arm collaborative mango harvesting robotic system. The results provide technical support for automated mango harvesting.