This paper presents a viable approach for incorporating collision avoidance strategies into existing guidance and control systems on marine vessels. We propose a method that facilitates the use of simulation-based Model Predictive Control (MPC) for collision avoidance (COLAV) on marine vessels. Any COLAV strategy to be applied in real traffic must adhere to the international regulations for preventing collisions at sea (COLREGS). The proposed MPC COLAV method does not rely on an accurate model of the guidance system to achieve vessel behaviors that are compliant with the COLREGS. Rather, it depends on transitional costs in the MPC objective for collision avoidance maneuvers that are being executed by the marine vessel. Hence, it is straightforward to implement the MPC COLAV on different vessels without specific knowledge of the vessel's guidance strategy. Moreover, it offers the possibility to switch between different (possibly application specific) guidance strategies on the same vessel while running the same MPC COLAV algorithm. We present results from full scale experiments that show the viability of our method in different collision avoidance scenarios.

/pdf/mpc-based-collision-avoidance-strategy-for-existing-marine-4plvva42gs.pdf

MPC-based Collision Avoidance Strategy for Existing Marine Vessel Guidance Systems

Autonomous collision detection and avoidance for ARAGON USV: Development and field tests

A framework to model the STPA hierarchical control structure of an autonomous ship

/pdf/conceptualizing-the-key-features-of-cyber-physical-systems-u8acsau1zd.pdf

Conceptualizing the key features of cyber‐physical systems in a multi‐layered representation for safety and security analysis

https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.21919

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

Autonomous surface vehicles and maritime autonomous surface ships must rely on sense-and-avoid systems for navigating safely among other ships. The main objective of this paper is to present examples of such systems, and their verification in full-scale collision avoidance experiments as part of the research project "Sensor fusion and collision avoidance for autonomous surface vehicles" (Autosea). Lessons learned from the progression of experiments have led to increasing robustness of the methods, and provide a foundation for several important topics of further research in the near future.

https://iopscience.iop.org/article/10.1088/1742-6596/1357/1/012020/pdf

The Autosea project: Developing closed-loop target tracking and collision avoidance systems

This paper addresses the challenges of making safe and predictable collision avoidance decisions considering uncertainties related to maritime radar tracking. When a maritime radar is used for autonomous collision avoidance, strategies for handling uncertain obstacle tracks, false tracks, and track loss become necessary. Robust decisions are needed in order to achieve clear and predictable actions according to the international regulations for preventing collisions at sea (COLREGs). We present robustness considerations and results of using an Integrated Probabilistic Data Association (IPDA) tracking method with a collision avoidance method based on Model Predictive Control. The results are from full-scale experiments that cover challenging multiple dynamic obstacle scenarios, including realistic vessel interactions where some obstacles obey COLREGs, while others do not.

/pdf/autonomous-colregs-compliant-decision-making-using-maritime-27qh2gihe5.pdf

Autonomous COLREGs-Compliant Decision Making using Maritime Radar Tracking and Model Predictive Control

If an Autonomous Surface Vehicle (ASV) is to be operated at sea in the proximity of other vessels it must be equipped with a Collision Avoidance System (CAS). This system must comply with the rules for avoiding collision that govern the seas, the International Regulations for Preventing Collisions at Sea (COLREGS). In this thesis a COLREGS compliant CAS using Simulation-Based Model Predictive Control (SB-MPC) has been implemented. The four main scenarios used to test the method’s behavior and COLREGS compliance is: headon, overtaking, and crossing from port and starboard. Several more complicated scenarios are also studied to see how the method deals with more complicated situations. The tests were performed within a Robotic Operating System (ROS) framework using a non-linear ship model to predict the ASV’s movements. The tests were then repeated using a linear model. The behavior of the system is discussed and compared with the behavior of the ASV equipped with a CAS based on the Velocity Obstacle (VO) algorithm. After the simulation study was completed the CAS was also tested on the On-board System Simulator (OBS) of Maritime Robotics to prepare for full-scale experiments. These simulations were using live Automatic Identification System (AIS)-data from ships in the Trondheimsfjorden. The CAS performed well during the simulations, both within the ROS framework and in the OBS environment. However, finer tuning is still needed to get optimal performance. Full-scale testing was also attempted, but not completed, mostly due to the lack of suitable obstacle vessels.

Collision Avoidance for ASVs Using Model Predictive Control

The main question investigated is whether additional decision steps can improve vessel behavior produced by the collision avoidance method scenario based model predictive control (SBMPC). The method, which functions by predicting alternative paths resulting from a finite number of alternative control behaviors, then selecting which behavior to apply by use of a cost function, was originally formulated to allow switching between several behaviors on the prediction horizon. However, current implementations have been limited to a single control step. To compare the single-step and multi-step SBMPC, a simulation study was performed, where different configurations for the number, positioning and possible control actions were tested. In the course of the simulation study it became clear that identifying situations producing a significant difference between the two methods was difficult to identify and the multi-step SBMPC led to only minor improvements in very few scenarios. Nevertheless, multi-step decisions can be visualized to give better situational awareness, and also have additional benefits with other trajectory parameterizations and less uncertain predictions of other ship trajectories. 

I. B. Hagen

Papers

MPC-based Collision Avoidance Strategy for Existing Marine Vessel Guidance Systems

The Autosea project: Developing closed-loop target tracking and collision avoidance systems

Autonomous COLREGs-Compliant Decision Making using Maritime Radar Tracking and Model Predictive Control

Collision Avoidance for ASVs Using Model Predictive Control

Scenario-Based Model Predictive Control with Several Steps for COLREGS Compliant Ship Collision Avoidance