Path Planning and Tracking Control of a Tracked Orchard Mower Based on IRRT and F-PD

To address redundant and tortuous paths generated for tracked mowers in unstructured orchard environments and the limited adaptability of fixed-gain controllers to nonuniform-curvature paths, this study proposes a joint optimization method combining an improved rapidly exploring random tree (IRRT) planner with fuzzy PD (F-PD) tracking control. The planner uses an obstacle-density-based adaptive step size to balance search efficiency and obstacle-avoidance safety, an improved artificial potential field to bias random samples toward the goal, and cubic B-spline smoothing to generate continuous paths. Based on a differential-steering kinematic model, the F-PD controller uses heading error and its rate of change as inputs and adjusts proportional and derivative gains online through fuzzy inference. Across the three simulated environments, the average reductions in path length, node count, and computation time achieved by IRRT relative to conventional RRT were 10.6%, 11.7%, and 66.7%, respectively. The maximum lateral error of F-PD was 0.43 m, versus 1.42 m for PID. Field tests showed that the IRRT–F-PD combination reduced cumulative operation time and cumulative relative fuel consumption by 30.5% and 26.6%, respectively, compared with RRT–PID. The proposed method improves planning efficiency and curved-path tracking for autonomous orchard mowing.