TY - JOUR
T1 - Convex optimization-based parallel trajectory stitching in dynamic environments
T2 - A dual-layer trajectory planning framework
AU - Hua, Bikang
AU - Du, Zhaofeng
AU - Liu, Tianhao
AU - Chai, Runqi
AU - Xu, Jiping
AU - Chai, Senchun
N1 - Publisher Copyright:
© 2026 Elsevier Ltd
PY - 2026/5
Y1 - 2026/5
N2 - This paper presents and validates an efficient dual-layer trajectory planning framework for the dynamic environment. The upper-layer utilizes offline global planning to generate an optimal global trajectory in a static obstacle environment, while the lower-layer performs online local planning, enabling real-time obstacle avoidance by predicting the future states of the dynamic obstacle and employing appropriate avoidance strategies. The main novelty lies in the following aspects: firstly, a fault-tolerant dynamic obstacle avoidance strategy, along with an LSTM-based trajectory prediction network, enables flexible velocity planning or local trajectory replanning based on the situation; secondly, a convex optimization-based parallel stitching strategy for local trajectory replanning, where candidate trajectories are generated through parallel computation and the optimal stitching solution is greedily selected. During the parallel problem-solving process, the original problem is transformed into a convex optimization problem via linearization and convexification to enhance solution efficiency. Iterative numerical solving is applied, with Line Search steps introduced between iterations to reduce deviation from original constraints and further minimize approximation errors. Simulation and experimental results validate the effectiveness and practicality of the proposed framework.
AB - This paper presents and validates an efficient dual-layer trajectory planning framework for the dynamic environment. The upper-layer utilizes offline global planning to generate an optimal global trajectory in a static obstacle environment, while the lower-layer performs online local planning, enabling real-time obstacle avoidance by predicting the future states of the dynamic obstacle and employing appropriate avoidance strategies. The main novelty lies in the following aspects: firstly, a fault-tolerant dynamic obstacle avoidance strategy, along with an LSTM-based trajectory prediction network, enables flexible velocity planning or local trajectory replanning based on the situation; secondly, a convex optimization-based parallel stitching strategy for local trajectory replanning, where candidate trajectories are generated through parallel computation and the optimal stitching solution is greedily selected. During the parallel problem-solving process, the original problem is transformed into a convex optimization problem via linearization and convexification to enhance solution efficiency. Iterative numerical solving is applied, with Line Search steps introduced between iterations to reduce deviation from original constraints and further minimize approximation errors. Simulation and experimental results validate the effectiveness and practicality of the proposed framework.
KW - Convex optimization
KW - Long short-term memory network
KW - Optimal control
KW - Trajectory planning
UR - https://www.scopus.com/pages/publications/105029476216
U2 - 10.1016/j.conengprac.2026.106815
DO - 10.1016/j.conengprac.2026.106815
M3 - Article
AN - SCOPUS:105029476216
SN - 0967-0661
VL - 170
JO - Control Engineering Practice
JF - Control Engineering Practice
M1 - 106815
ER -