Objectives To address the challenges of path planning and tracking for underactuated ships in obstacle-filled waters, this paper proposes a ship path planning and tracking algorithm based on the rapidly exploring random tree (RRT*) and convex optimization.

Methods  The method utilizes the RRT* algorithm to sample and plan feasible paths in a grid environment, generating a sequence of key points for further optimization. For the key point sequence in the feasible path, optimization of the economic and safety aspects of the curve is achieved through limited memory BFGS (L-BFGS) convex optimization algorithm and cubic spline curves, yielding smoother and safer ship paths parameterized by time. Finally, model predictive control (MPC) algorithm is employed to generate ship control output sequences, enabling safe and efficient navigation around obstacles from the starting point to the destination.

Results Simulation results demonstrate that using this algorithm, ships can achieve efficient path planning and trajectory tracking, with path search time less than 2×10⁻⁵ s, path optimization time less than 0.5 seconds, and trajectory tracking absolute error less than 0.75 m.

Conclusions Simulation results indicate that the proposed path planning and tracking algorithm ensures effective path search and optimization for ships, providing a foundation for further research and industrial applications of autonomous surface vehicles.