This paper addresses the issue of obstacles encountered by intelligent vehicles during their movement in agricultural fields. The traditional artificial potential field (APF) method for obstacle avoidance often results in problems such as unreachable target points and the vehicle getting trapped in local minima, preventing it from moving. To overcome these issues, we propose dynamically adjusting the size of the potential field by incorporating the relative distance between the vehicle s real-time position and the target point as a criterion. Additionally, we apply the simulated annealing (SA) method, which uses its inherent search probability to escape local minima. Finally, path smoothing is performed to compensate for the shortcomings of the traditional APF algorithm. MATLAB simulations of the improved APF-based obstacle avoidance path planning confirm the feasibility of the proposed method.

Aiming at the problems of low tracking accuracy and more steering control times of the existing unilateral braking tracked vehicle tracking control algorithm, an adaptive path tracking algorithm for unilateral braking tracked vehicle based on Particle swarm optimization (PSO) is proposed. Based on the preview tracking model, the track vehicle path tracking method is studied; In order to improve the adaptive ability of the preview tracking model, a fitness function is constructed based on the tracking accuracy and steering control times. The lateral error is used as the main decision parameter, and the forward-looking distance in the preview tracking model is determined in real time by particle swarm optimization algorithm; In order to reduce the calculation time of particle swarm optimization and carry out local search as soon as possible, the inertia weight coefficient and particle state update strategy in PSO algorithm are improved, and chaos factor is introduced. In this paper, the tracking accuracy and steering control times of the algorithm are comprehensively evaluated through simulation and actual tests on the test platform of the modified 3b55 tracked transport vehicle. Compared with SSA algorithm, the improved PSO algorithm has faster convergence speed, higher tracking accuracy and fewer steering control times. The research results can provide innovative ideas and technical support for the automatic navigation technology of unilateral braking tracked vehicles.

Aiming at the problems of low tracking accuracy and more steering control times of the existing unilateral braking tracked vehicle tracking control algorithm, an adaptive path tracking algorithm for unilateral braking tracked vehicle based on Particle swarm optimization (PSO) is proposed. Based on the preview tracking model, the track vehicle path tracking method is studied; In order to improve the adaptive ability of the preview tracking model, a fitness function is constructed based on the tracking accuracy and steering control times. The lateral error is used as the main decision parameter, and the forward-looking distance in the preview tracking model is determined in real time by particle swarm optimization algorithm; In order to reduce the calculation time of particle swarm optimization and carry out local search as soon as possible, the inertia weight coefficient and particle state update strategy in PSO algorithm are improved, and chaos factor is introduced. In this paper, the tracking accuracy and steering control times of the algorithm are comprehensively evaluated through simulation and actual tests on the test platform of the modified 3b55 tracked transport vehicle. Compared with SSA algorithm, the improved PSO algorithm has faster convergence speed, higher tracking accuracy and fewer steering control times. The research results can provide innovative ideas and technical support for the automatic navigation technology of unilateral braking tracked vehicles.

The paper proposes a tracked vehicle slope path track control algorithm based on MPC hilly mountainous area motor differential control. First, a tracked vehicle slope driving slip model is established to achieve the accurate estimation slope position. Second, the desired acceleration and desired angular acceleration are obtained as inputs by incorporating the intended linear and angular velocities, and the actual motivation required of the tracked vehicle when steering the vehicle on the slope is estimated by the MPC, and then the driving force is obtained as the actual control signal through the road surface response and kinematics solving to achieve the slope control of tracked vehicles. The actual control signal is obtained from the driving force through the road surface response and kinematics solution to realize the tracked vehicle slope path tracking control. The test outcomes demonstrate that our path tracking algorithm exhibits superior performance compared to the proportion integral differential (PID) control method during the vehicle's travel on a slope.