From Multi-Modal Paths to Executable Trajectories: A Trajectory Planning Framework for 4WIS Robots
Robotics
Summary
The authors developed a new way to plan paths for robots that can steer all four wheels independently, making them very good at moving in tight spaces. They created a two-part system: first, an improved search method that considers different ways the robot can move, and second, a way to smooth out the planned path so the robot can follow it easily. Their experiments show this method is safe, fast, and accurate, and it worked well on a real robot. This provides a better way to plan robot movements using multiple motion modes.
Authors
Runjiao Bao, Lin Zhang, Yongkang Xu, Shoukun Wang
Abstract
Four-wheel independent steering (4WIS) mobile robots support multiple motion modes, offering high maneuverability in narrow and complex environments. However, existing planning methods often fail to fully exploit these capabilities, leading to suboptimal trajectory quality. To address this limitation, this paper proposes a multi-modal global trajectory planning framework that couples mode-augmented front-end search with mode-consistent segment-wise trajectory optimization. In the front-end stage, Hybrid A* is extended to a four-dimensional state space incorporating motion modes, while mode-switching-aware cost and heuristic functions embed mode decisions into the global search process. Multi-modal Reeds-Shepp curves and an intelligent terminal connection strategy are further designed to improve search efficiency. In the back-end stage, a segment-wise trajectory optimization framework based on an improved iterative safe corridor scheme is developed to convert discrete multi-modal paths into smooth, kinematically feasible trajectories with stationary mode transitions. Experimental results show that the proposed method achieves the best overall performance in safety, arrival time, terminal accuracy and computation time. Real-world experiments on a physical 4WIS robot further validate the practical effectiveness and executability of the generated trajectories, providing a flexible and high-performance solution for multi-modal mobile robot trajectory planning.