Research on Trajectory Planning Method for 4WIS Vehicles Based on Scene Complexity Classification Network and Guided Point Mechanism

doi:10.3901/JME.260282

Abstract

Abstract: Four-wheel independent steering(4WIS) vehicles have attracted widespread attention due to their superior maneuverability. However, existing trajectory planning methods insufficiently consider the multiple motion modes of 4WIS vehicles, scene complexity, and obstacle attributes, limiting their flexibility in narrow and cluttered spaces and resulting in low planning efficiency or even failure. To address this, this study proposes a 4WIS vehicle trajectory planning framework based on an optimal control problem(OCP). First, a scene complexity binary classification network is developed based on environmental images and vehicle state information to accurately identify complex and simple scenes. Second, a trajectory guidance strategy for complex scenarios is designed, constructing a set of guided points based on a prior A^* path and decomposing the task into local subtasks between guided points to improve planning efficiency. Third, a Hybrid A^* algorithm tailored for 4WIS vehicles is constructed, incorporating a node expansion mechanism that integrates Ackermann steering, diagonal movement, and zero-turn rotation, along with corresponding node cost functions and mode-switching cost functions. Finally, a trajectory optimization OCP framework considering obstacle attributes is established, introducing logical constraints for “compressible obstacles” to limit the speed when passing over them, thereby enhancing vehicle passability while ensuring safety. Simulation results show that, in typical complex environments with dense obstacles, narrow passages, and significant differences in start and goal positions and orientations, the proposed method improves the planning success rate by 50%, increases traversal efficiency by 40.26%, and enhances computational efficiency by 44.89% compared with the Hybrid A^* algorithm, significantly boosting the trajectory planning performance of 4WIS vehicles.

Key words: four-wheel independent steering(4WIS) vehicles, trajectory planning, scene complexity classification network, trajectory guided strategy, 4WIS Hybrid A^*, obstacle logical constraints

CLC Number:

U493
TP18

TENG Jingjia, LI Yang, HU Manjiang, XIONG Shancheng, LI Guofa. Research on Trajectory Planning Method for 4WIS Vehicles Based on Scene Complexity Classification Network and Guided Point Mechanism[J]. Journal of Mechanical Engineering, 2026, 62(8): 432-449.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260282

http://www.cjmenet.com.cn/EN/Y2026/V62/I8/432

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