Dynamic weight matching TEB local path planning improved by PSO for car-like robots

Local path planning for car-like robots often relies on the Timed Elastic Band (TEB) algorithm, yet conventional implementations suffer from two critical limitations: insufficient trajectory smoothness resulting in jerky motion and mechanical wear, and high sensitivity to weight parameters requiring laborious manual tuning that fails to generalize across diverse scenarios. To address these challenges, this paper proposes a novel framework that enhances TEB through explicit smoothness constraints while eliminating manual tuning via Particle Swarm Optimization (PSO). We first augment the TEB objective function with higher-order smoothness and jerk constraints to fundamentally improve trajectory quality and dynamic feasibility. Second, we introduce a systematic offline-online framework where PSO discovers optimal weight configurations across 150 representative training scenarios, each characterized by a three-dimensional feature vector capturing narrowness, turning complexity, and heading deviation. The discovered scenario-weight mappings are stored in a knowledge base enabling real-time weight retrieval through efficient similarity matching during online operation. Comprehensive evaluation across three progressively challenging test scenarios with 90 independent runs demonstrates statistically significant improvements: PSO-TEB achieves 34–38% execution time reduction, 39–53% acceleration smoothness improvement, and 100% success rate in dynamic environments compared to 0–20% for baseline methods. Real-world experiments on a car-like robotic platform validate practical viability with sub-millisecond online weight matching overhead. By shifting computational burden offline and enabling scenario-adaptive parameter adjustment, our approach enhances robustness and practical applicability of TEB for car-like robots in complex real-world conditions.