Path tracking control method for tracked agricultural vehicles based on slip-aware look-ahead point offset

Abstract

Introduction: Tracked agricultural vehicles operating in complex farmland environments are prone to track slip, which degrades path-tracking accuracy and may lead to unstable motion. To address the limitations of conventional geometric tracking algorithms under slip conditions, this study proposes a slip-aware look-ahead point offset path-tracking control method for tracked agricultural machinery.

Methods: An extended Kalman filter (EKF) is developed to fuse RTK-IMU pose measurements with track wheel-speed feedback, enabling real-time estimation of left and right track slip ratios. Based on the estimated slip difference, a target-point offset compensation mechanism is constructed, and the offset angle is optimized online using an improved particle swarm optimization (PSO) algorithm with a Chebyshev-window-based inertia weight strategy. In addition, a fuzzy controller is employed to adaptively adjust the look-ahead distance according to vehicle speed and path curvature, while a first-order low-pass filter is applied to smooth the commanded velocities.

Results: Simulation results demonstrate that the proposed method significantly reduces lateral tracking errors and maintains smooth trajectories under severe slip conditions. Field experiments conducted at speeds of 0.35 m/s and 0.75 m/s show that the proposed method reduces the maximum lateral deviation by 78.1% and the average deviation by 50.6% compared with the traditional fuzzy pure pursuit algorithm. At 0.75 m/s, the maximum and average deviations are further reduced by 63.1% and 57.6%, respectively.

Discussion: The results confirm that incorporating slip estimation and slip-aware target-point offset compensation effectively enhances path-tracking accuracy and robustness for tracked agricultural vehicles operating on soft and high-slip terrain. The proposed lightweight control framework provides a practical and reliable solution for autonomous navigation and plant-protection operations in complex farmland environments.

Keywords: extended Kalman filter; improved particle swarm optimization; pure pursuit algorithm; slip compensation; tracked agricultural machinery.

Figure 1

Kinematic modeling schematic of a tracked agricultural vehicle.

Figure 2

Geometric relationship schematic of the pure pursuit path-tracking algorithm.

Figure 3

Overall flowchart of the control system.

Figure 4

Schematic of target point offset and trajectory response under slip compensation.

Figure 5

Distribution diagram of membership functions.

Figure 6

Surface of fuzzy control rules.

Figure 7

Comparison of variation trends under different inertia weight decay strategies.

Figure 8

Schematic of path and slip condition settings. (a) Reference path diagram, (b) Path curvature variation curve, (c) Left and right track slip ratio variation diagram.

Figure 9

Path-tracking comparison results (including locally enlarged view).

Figure 10

Variation curve of offset compensation angle with time steps.

Figure 11

Path-tracking experimental platform of tracked agricultural vehicle and composition of the control system. (a) Tracked agricultural vehicle (real machine), (b) Control system composition diagram.

Figure 12

Real-vehicle experiment of navigation tracking.

Figure 13

Path-tracking comparison at different speeds. (a) Set vehicle speed v=0.35m/s, (b) Set vehicle speed v=0.75m/s.

Figure 14

Lateral deviation comparison curves under different speed conditions.