Collision-free path planning for a guava-harvesting robot based on recurrent deep reinforcement learning

In unstructured orchard environments, picking a target fruit without colliding with neighboring branches is a significant challenge for guava-harvesting robots. This paper introduces a fast and robust collision-free pathplanning method based on deep reinforcement learning. A recurrent neural network is first adopted to remember and exploit the past states observed by the robot, then a deep deterministic policy gradient algorithm (DDPG) predicts a collision-free path from the states. A simulation environment is developed and its parameters are randomized during the training phase to enable recurrent DDPG to generalize to real-world scenarios. We also introduce an image processing method that uses a deep neural network to detect obstacles and uses many threedimensional line segments to approximate the obstacles. Simulations show that recurrent DDPG only needs 29 ms to plan a collision-free path with a success rate of 90.90%. Field tests show that recurrent DDPG can increase grasp, detachment, and harvest success rates by 19.43%, 9.11%, and 10.97%, respectively, compared to cases where no collision-free path-planning algorithm is implemented. Recurrent DDPG strikes a strong balance between efficiency and robustness and may be suitable for other fruits.