Low-Injury Rubber Tapping Robots: A Novel PSO-PID Approach for Adaptive Depth Control in Hevea Brasiliensis

Rubber tapping robots represent a significant research direction in modern robotics in agricultural automation. Nevertheless, natural rubber tapping robots encounter considerable challenges in achieving precise tapping, particularly in controlling tapping depth, due to the lack of suitable control algorithms. To solve this problem, an improved Particle Swarm Optimization/Proportional–Integral–Derivative (PSO-PID) control method has been proposed in this paper. It enhances the inertia weight of the particle swarm by introducing adaptive inertia weight, solving the shortcomings of the traditional PSO algorithm, such as insufficient local search ability and early convergence. The experimental results show that the rubber tapping depth system based on the improved PSO-PID algorithm has high responsiveness and robustness, with an average settling time of 0.419 s and an overshoot that can be kept below 2.5%. The depth control accuracy, robustness and convergence speed of the system are significantly better than other well-known optimization algorithms. At a tapping depth of 3.0 mm, the injury rate was reduced to 2%, surpassing the level of skilled manual tapping workers. It has been proven that this method can effectively solve the key problem of accurate depth control in current rubber tapping.