Ship Collision Avoidance and COLREGS Compliance Using Simulation-Based Control Behavior Selection With Predictive Hazard Assessment

Abstract

Abstract-This paper describes a concept for a collision avoidance system for ships, based on model predictive control. A finite set of alternative control behaviors are generated by varying two parameters: offsets to the guidance course angle commanded to the autopilot, and changes to the propulsion command ranging from nominal speed to full reverse. Using simulated predictions of the trajectories of the obstacles and ship, the compliance with COLREGS and collision hazards associated with each of the alternative control behaviors are evaluated on a finite prediction horizon, and the optimal control behavior is selected. Robustness to sensing error, predicted obstacle behavior, and environmental conditions can be ensured by evaluating multiple scenarios for each control behavior. The method is conceptually and computationally simple and yet quite versatile as it can account for the dynamics of the ship, the dynamics of the steering and propulsion system, forces due to wind and ocean current, and any number of obstacles. Simulations show that the method is effective and can manage complex scenarios with multiple dynamic obstacles and uncertainty associated with sensors and predictions.

MPC-Based mid-level collision avoidance for asvs using nonlinear programming

“…However, if it is apparent that the giveway vessel is not taking action to avoid collision, the stand-on vessel is required to apply actions to best avoid collision. Rule 8 is considered in [10] by limiting the possible actions, resulting in distinct observable actions. These rules are, however, seldom considered in the literature.…”

“…and can encode more sophisticated criteria for the behavior, for instance generating observable maneuvers which are optimal in a global sense. Examples of deliberate algorithms include rapidly-exploring random trees (RRT) [6], graph search algorithms such as A* and D* [7,8] and constrained nonlinear optimization [9,10]. A downside of deliberate algorithms is their computational complexity, which results in high computational requirements and possibly challenges for real-time implementation.…”

“…Constrained optimization in the form of model predictive control (MPC) has already been applied for automotive COLAV [12,13]. The use of MPC for ASV COLAV has been investigated in [10], however only using brute-force techniques by discretization of the search space in a finite number of control inputs. In this paper, we present an MPC algorithm for mid-level COLAV, implemented using NLP and solved using the IPOPT [14] solver in the CASADI [15] framework.…”

Autonomous maritime collision avoidance: Field verification of autonomous surface vehicle behavior in challenging scenarios

“…Telemetron, which is Maritime Robotics’ research and development vessel was therefore prepared for a test week in the North Sea, from November 20 to 24, 2017. In close collaboration with the Norwegian University of Science and Technology (NTNU), Maritime Robotics equipped Telemetron with the model predictive control (MPC)‐based collision avoidance strategy described in Hagen, Kufoalor, Brekke, and Johansen () and Johansen, Perez, and Cristofaro (). Since its first implementation and deployment on the Telemetron vessel in 2016, the MPC strategy has been tested and refined through experimental field work consisting of several campaigns in the Trondheimsfjord in Norway.…”

An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea Environments

“…Model predictive control (MPC), which combines path planning with on-line stability and convergence guarantees, is increasingly being applied to collision avoidance problem [27]. One significant study can be found in the work undertaken by Johansen [28], who applied MPC in the CAS of ship with a simple external environment. However in contrast to ordinary ships, considering that USVs have the characteristics of high speed and smaller volume, the CAS needs shorter computation times and stronger robustness.…”