Trajectory-Free Motion Planning of an Unmanned Surface Vehicle Based on MPC and Sparse Neighborhood Graph

Abstract

Unmanned Surface Vehicles (USV) have gained significant attention in military, science, and research applications in recent years. The development of new USV systems and increased application domain of these platforms has necessitated the development of new motion planning methods to improve the autonomy level of USVs and provide safe and robust navigation across unpredictable marine environments. This study proposes a feedback motion planning and control methodology for dynamic fully-and under-actuated USV models built on the recently introduced sparse random neighborhood graphs and constrained nonlinear Model Predictive Control (MPC). This approach employs a feedback motion planning strategy based on sparsely connected obstacle-free regions and the sequential composition of MPC policies. The algorithm generates a sparse neighborhood graph consisting of connected rectangular zones in the discrete planning phase. Inside each node (rectangular region), an MPC-based online feedback control policy funnels the USV with nonlinear dynamics from one rectangle to the other in the network, ensuring no constraint violation on state and input variables occurs. We systematically test the proposed algorithms in different simulation scenarios, including an extreme actuator noise scenario, to test the algorithm’s validity, effectiveness, and robustness.

Keywords

• Nonlinear model predictive control
• feedback motion planning
• sampling-based motion planning
• unmanned surface vehicles