Reinforcement Learning‐Enhanced Path‐Planning Method for Orchard Robots

ABSTRACT Path planning for orchard robots is one of the most fundamental technologies of smart agriculture. Introducing reinforcement learning (RL) into path planning has become a promising approach at the frontier of robotics research, addressing the problem that existing methods primarily focus on the optimal path at the immediate moment, lack a holistic perspective, and fail to achieve optimal overall performance. However, the sim‐to‐real issue presents a critical challenge for implementing RL in orchard robots. To solve this issue, an RL‐enhanced path‐planning method is proposed for orchard robots. The proposed method avoids directly using RL to generate a path, thereby eliminating the primary source of the sim‐to‐real problem in RL. In the proposed method, multiple candidate paths are computed using a Bézier curve and are physically navigable for robots, leveraging LiDAR to dynamically update the environmental map. Meanwhile, a comprehensive reward function is designed for RL to achieve optimal path planning. Subsequently, RL is employed to select the optimal path among multiple candidate paths, thereby avoiding the sim‐to‐real problem in complex orchard environments. Finally, validation tests are carried out in both simulation and field environments. The experimental results show that the proposed method effectively addresses the sim‐to‐real challenges of RL and achieves optimal path planning.