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This paper introduces a novel trajectory planning framework for autonomous surface vehicles that utilizes a turning circle-based control barrier function (TC-CBF) integrated with model predictive control (MPC) to navigate dynamic environments without predefined guide paths. By considering the vehicle's nonholonomic motion and finite turning capabilities, the TC-CBF effectively delineates safe avoidance regions and enables distinct left- and right-turning modes, enhancing trajectory optimization. Extensive simulations reveal that this approach significantly outperforms traditional single-mode MPC methods, achieving higher success rates and fewer safety violations across various traffic densities.
Turning circle-based control barrier functions enable autonomous surface vehicles to navigate complex environments without predefined paths, achieving superior safety and efficiency.
This paper presents a guide path-free multimodal trajectory planning framework for autonomous surface vehicles operating in dynamic environments. The proposed method integrates model predictive control (MPC) with a turning circle-based control barrier function (TC-CBF). Unlike conventional Euclidean distance-based CBFs (ED-CBFs), which evaluate safety solely based on proximity, the TC-CBF accounts for the nonholonomic motion and finite turning capability of a surface vehicle. Its geometric formulation identifies feasible avoidance regions according to the vehicle's turning circles and generates distinct left- and right-turning avoidance modes. These modes allow the optimization solver to explore and select topologically different trajectories without relying on globally planned guide paths, as required by many conventional multimodal planning approaches. By embedding the avoidance direction directly into the safety constraint, the proposed framework alleviates the local-minimum and deadlock problems of single-mode MPC while maintaining computational efficiency. Extensive simulations involving multiple moving vessels demonstrate that the proposed method achieves higher success rates, fewer safety violations, and smaller residual violations than single-mode baselines across all tested traffic densities.
