OBSTACLE AVOIDANCE PLANNING OF GRAPE PICKING ROBOTS BASED ON DEEP REINFORCEMENT LEARNING

Given that picking robots are faced with many picking tasks in the field operation environment and the target and obstacles are located at random and uncertain positions, an obstacle avoidance planning method for the picking path of virtual robots based on deep reinforcement learning was proposed to achieve rapid route planning of robots under a lot of uncertain tasks. Next, the random motion strategy of virtual robots was set according to the physical structure of robot bodies. By comparatively analyzing the advantages and disadvantages of the observed values input by different networks, an environmental observation set was established in combination with actual picking behaviors as the network input; then, a reward function was established by introducing the idea of target attraction and obstacle repulsion contained in the artificial potential field method, aiming to evaluate the behavior of virtual robots and increase the success rate of obstacle avoidance. The results of the simulation experiment showed that the success rate obtained by virtual robots in completing the picking task reached 95.5% under obstacles set at different positions. The coverage path length of the deep reinforcement learning algorithm was reduced by 272.79 in compared with that of genetic algorithm, with a reduction rate of 5.09%. The total time consumed by navigation was 1549.24 s, which was 83.15 s shorter than that of the traditional algorithm. The study results manifest that the system can efficiently guide virtual robots to rapidly reach the random picking points on the premise of avoiding obstacles, meet picking task requirements and provide theoretical and technical support for the picking path planning of real robots.