Dynamically weighted perceptual obstacle avoidance algorithm for UGV navigation in unknown rugged terrains

To address the navigation challenges of unmanned ground vehicles (UGVs) in unknown, unstructured environments, we propose the Dynamic Weighted Perception Avoidance Algorithm (DWPA). DWPA features LiDAR-based real-time perception and a dynamic weighting mechanism that autonomously balances goal-directed navigation with obstacle avoidance. A key innovation is the introduction of a hysteresis factor with nonlinear low-pass filtering properties, which suppresses oscillatory control commands triggered by perceptual jumps and enhances robustness. Theoretical reliability is established through Lyapunov stability analysis and control barrier function-based safety proofs. Co-simulations (MATLAB/V-REP) and physical experiments validate DWPA against the artificial potential field (APF) and model predictive control (MPC) methods. Results show that DWPA generates smoother, safer trajectories than MPC while maintaining a larger safety margin, and reduces path length by over 14.4% compared to APF. Its computational efficiency and rapid response make it particularly effective for complex, dynamically changing terrains.