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

Abstract

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.

Hybrid Path Planning for Unmanned Surface Vehicles in Inland Rivers Based on Collision Avoidance Regulations

“…From another perspective, because of the receding horizon characteristic of the MPC algorithm, it has good robustness against water flow disturbances. Compared with [16], this study removed the distance penalty term from the cost function and considered the distance factor during encounter-situation judgment. This not only did not reduce the performance of the algorithm, but also simplified the cost function and improved the efficiency of the algorithm.…”

“…The speed of obstacle ships also varied during the simulation, but due to the robustness of the MPC algorithm, the "Jinghai-I" USV still effectively avoided obstacles. For the performance of the algorithm in other encounter scenarios, please refer to the authors' previous study [16].…”

“…The model predictive control algorithm is a numerical-optimization algorithm that can effectively handle nonlinear optimization problems with multiple inputs and outputs, It has good robustness with respect to environmental disturbances. Based on previous research [16], this section discusses the optimization of the MPC algorithm according to the actual needs of the project.…”

“…The local path planning algorithm based on the MPC algorithm [16] was improved according to the actual requirements of the project. To verify the performance of the improved algorithm, three simulation results using three cost functions were carried out under the conditions of water flow and no water flow.…”

Collision avoidance for autonomous ship using deep reinforcement learning and prior-knowledge-based approximate representation

“…Further, there are bottlenecks in the processing of complex working conditions and the improvement of algorithm performance (Tam et al, 2009;Wang et al, 2022). To solve these problems, many scholars have proposed soft computing methods, such as genetic algorithm (Tsou et al, 2010), velocity Obstacle (Wang et al, 2020), fuzzy logic (Fiskin et al, 2021), geometric calculation (Ding et al, 2021), and model predictive control (Yuan and Gao, 2022). However, these soft computing methods have exposed their limitations in the MASS collision avoidance applications, among which is the difficulty of tackling new collision avoidance risks due to lack of scene adaptability after a MASS attempt to avoids multiple ships successively (Burmeister and Constapel, 2021).…”