TY - GEN
T1 - MPC-Based Collision-Free Trajectory Planning for Multi-UGV Formation with Yaw Constraints
AU - Tang, Denglin
AU - Wang, Haopeng
AU - Liu, Yongfang
AU - Zhao, Yu
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - This paper presents a model predictive control (MPC)-based trajectory planning method for multi-unmanned ground vehicle (UGV) formation, targeting collision-free navigation in a cluttered environment while satisfying the terminal yaw angle constraint. The proposed approach integrates Buffered Voronoi Cell (BVC) to ensure internal collision avoidance and constructs a feasible region to avoid external obstacles. A yaw error term is incorporated into the formation optimization to achieve precise orientation control, and the formation shape is adaptively optimized to accommodate narrow passages. Furthermore, an adaptive guidance point selection strategy is designed to balance path feasibility and formation stability. Simulation results demonstrate the effectiveness of the proposed method in complex environments, showing accurate terminal yaw control and superior performance in terms of calculation cost and planning efficiency.
AB - This paper presents a model predictive control (MPC)-based trajectory planning method for multi-unmanned ground vehicle (UGV) formation, targeting collision-free navigation in a cluttered environment while satisfying the terminal yaw angle constraint. The proposed approach integrates Buffered Voronoi Cell (BVC) to ensure internal collision avoidance and constructs a feasible region to avoid external obstacles. A yaw error term is incorporated into the formation optimization to achieve precise orientation control, and the formation shape is adaptively optimized to accommodate narrow passages. Furthermore, an adaptive guidance point selection strategy is designed to balance path feasibility and formation stability. Simulation results demonstrate the effectiveness of the proposed method in complex environments, showing accurate terminal yaw control and superior performance in terms of calculation cost and planning efficiency.
KW - Model predictive control
KW - collision-free motion
KW - formation optimization
KW - trajectory planning
KW - yaw angle constraint
UR - https://www.scopus.com/pages/publications/105042703836
U2 - 10.1007/978-981-95-8435-2_27
DO - 10.1007/978-981-95-8435-2_27
M3 - 会议稿件
AN - SCOPUS:105042703836
SN - 9789819584345
T3 - Lecture Notes in Electrical Engineering
SP - 332
EP - 345
BT - Proceedings of 2025 9th Chinese Conference on Swarm Intelligence and Cooperative Control - Swarm Control Technologies
A2 - Wang, Qing
A2 - Dong, Xiwang
A2 - Song, Peng
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2025
Y2 - 31 October 2025 through 3 November 2025
ER -