Artificial Potential Field-Based Kinematic Bidirectional-RRT for Robot- Trailer Kinematics

Autonomous mobile robot systems with trailers are gaining traction in logistics, industrial and construction settings due to their ability to safely transport heavy loads. However, path planning for these systems is challenging due to their complex nonlinear dynamics. Existing methods follow a hierarchical approach to navigation, charting an initial global plan and addressing trailer kinematics locally using advanced control techniques. This approach often results in inefficient paths, complicating the task of local controllers. Moreover, integrating all potential robot configurations with kinematic constraints into global planning for large-scale, complex, and unstructured environments escalates computational demands. This paper proposes a novel approach that incorporates kinematic constraints directly into the global planning phase using an Artificial Potential Field-based Kinematic Bidirectional-RRT (APF-based KB-RRT) algorithm specifically adapted for an ackermann robot with a trailer. By addressing these constraints early in the planning process, we aim to improve overall path efficiency. To address the nonlinearities in the trailer kinematics, we employ the Newton-Raphson method. Simulation results demonstrate the efficacy of our proposed methodology in generating precise and efficient motion plans, characterized by minimal computational complexity. Concurrently, qualitative findings underscore the robustness of our approach, particularly evident in challenging scenarios, such as initial trailer positions close to folding angles.