Model Predictive Control-Based Collision Avoidance for Autonomous Surface Vehicles in Congested Inland Waters

Compared with open waters, congested inland waters have narrow waterways, many river-crossing bridges, a high density of navigation, and high current velocity in some sections. In this study, an improved collision avoidance algorithm based on model predictive control (MPC) is proposed to solve the problem of collision avoidance for autonomous surface vehicles (ASVs) in congested inland waters. First, considering the influence of current, the collision avoidance problem of ASVs is transformed into a nonlinear programming problem, and the kinematics of ASVs and the boundary of the channel are regarded as its inequality constraints. Next, since ASVs cannot perform large-scale collision avoidance in congested inland waters, the strategy of reducing the speed and slightly changing the yaw angle is adopted to realize collision avoidance. Then, an improved dynamic bumper model is used to model the safe zone of ASVs and dynamic obstacles, which improves the efficiency of the algorithm and the safety of ASVs. Finally, the collision avoidance rules and the evaluation function of the collision avoidance maneuver are constructed in the cost function of the algorithm. The simulation experiments in different encounter scenarios show that the proposed algorithm significantly improves the rationality and compliance of ASVs’ autonomous collision avoidance in congested inland waters.