EPSO-based rigid robotic arm for obstacle avoidance object grasping

The smart orchard tournament of the robot developer competition requires participants to utilise a robotic arm equipped with a monocular camera to grasp ping-pong balls bearing fruit patterns. Common issues include low object recognition accuracy, inefficient performance, and grasping failures due to collisions with obstacles. This paper proposes a novel object grasping framework, EPSO-TPPP-YOLOv8, based on a rigid robotic arm for the task of fruit picking. In the safe distance analysis section, the two obstacles, cylinder and sphere, are first modelled. The robotic arm is then considered as an object consisting of several cylinders. The formula for calculating the safe distance between the robot arm and the obstacle is provided, and the design of the obstacle avoidance path is guided according to the calculation results. In the robot arm path planning section, the hyper-parameters of the angles of each robot arm joint are first determined, and then particle swarm optimization based on a novel set of strategies and evaluation criteria is used to generate the desired paths. The findings reveal that, in the absence of human intervention, the proposed EPSO algorithm reliably identifies paths that adhere to safety boundary limits within 50 iterations. The EPSO-TPPP framework, when operated under human guidance, has been demonstrated to achieve this within 10 iterations. In the subsequent phase of vision-based object recognition phase, a distinctive dataset is constructed based on the specified task. It is an established fact that simulation and field experiments have demonstrated the efficacy of the robot arm's obstacle-avoidance grasping functionality.