97 research outputs found
A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning
Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off.Peer reviewe
COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning
Path Following and Collision Avoidance, be it for unmanned surface vessels or
other autonomous vehicles, are two fundamental guidance problems in robotics.
For many decades, they have been subject to academic study, leading to a vast
number of proposed approaches. However, they have mostly been treated as
separate problems, and have typically relied on non-linear first-principles
models with parameters that can only be determined experimentally. The rise of
Deep Reinforcement Learning (DRL) in recent years suggests an alternative
approach: end-to-end learning of the optimal guidance policy from scratch by
means of a trial-and-error based approach. In this article, we explore the
potential of Proximal Policy Optimization (PPO), a DRL algorithm with
demonstrated state-of-the-art performance on Continuous Control tasks, when
applied to the dual-objective problem of controlling an underactuated
Autonomous Surface Vehicle in a COLREGs compliant manner such that it follows
an a priori known desired path while avoiding collisions with other vessels
along the way. Based on high-fidelity elevation and AIS tracking data from the
Trondheim Fjord, an inlet of the Norwegian sea, we evaluate the trained agent's
performance in challenging, dynamic real-world scenarios where the ultimate
success of the agent rests upon its ability to navigate non-uniform marine
terrain while handling challenging, but realistic vessel encounters
A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning
Machine Learning (ML) techniques have gained significant traction as a means
of improving the autonomy of marine vehicles over the last few years. This
article surveys the recent ML approaches utilised for ship collision avoidance
(COLAV) and mission planning. Following an overview of the ever-expanding ML
exploitation for maritime vehicles, key topics in the mission planning of ships
are outlined. Notable papers with direct and indirect applications to the COLAV
subject are technically reviewed and compared. Critiques, challenges, and
future directions are also identified. The outcome clearly demonstrates the
thriving research in this field, even though commercial marine ships
incorporating machine intelligence able to perform autonomously under all
operating conditions are still a long way off
Path planning and collision avoidance for autonomous surface vehicles II: a comparative study of algorithms
Artificial intelligence is an enabling technology for autonomous surface vehicles, with methods such as evolutionary algorithms, artificial potential fields, fast marching methods, and many others becoming increasingly popular for solving problems such as path planning and collision avoidance. However, there currently is no unified way to evaluate the performance of different algorithms, for example with regard to safety or risk. This paper is a step in that direction and offers a comparative study of current state-of-the art path planning and collision avoidance algorithms for autonomous surface vehicles. Across 45 selected papers, we compare important performance properties of the proposed algorithms related to the vessel and the environment it is operating in. We also analyse how safety is incorporated, and what components constitute the objective function in these algorithms. Finally, we focus on comparing advantages and limitations of the 45 analysed papers. A key finding is the need for a unified platform for evaluating and comparing the performance of algorithms under a large set of possible real-world scenarios
A Locking Sweeping Method Based Path Planning for Unmanned Surface Vehicles in Dynamic Maritime Environments
Unmanned surface vehicles (USVs) are new marine intelligent platforms that can autonomously operate in various ocean environments with intelligent decision-making capability. As one of key technologies enabling such a capability, path planning algorithms underpin the navigation and motion control of USVs by providing optimized navigational trajectories. To accommodate complex maritime environments that include various static/moving obstacles, it is important to develop a computational efficient path planning algorithm for USVs so that real-time operation can be effectively carried out. This paper therefore proposes a new algorithm based on the fast sweeping method, named the locking sweeping method (LSM). Compared with other conventional path planning algorithms, the proposed LSM has an improved computational efficiency and can be well applied in dynamic environments that have multiple moving obstacles. When generating an optimal collision-free path, moving obstacles are modelled with ship domains that are calculated based upon ships’ velocities. To evaluate the effectiveness of the algorithm, particularly the capacity in dealing with practical environments, three different sets of simulations were undertaken in environments built using electronic nautical charts (ENCs). Results show that the proposed algorithm can effectively cope with complex maritime traffic scenarios by generating smooth and safe trajectories
The angle guidance path planning algorithms for unmanned surface vehicle formations by using the fast marching method
By deploying multiple USVs as a formation fleet, benefits such as wide mission area, improved system autonomy and increased fault-tolerant resilience can be achieved. To efficiently and effectively navigate the USV formation, path planning algorithms are required to generate optimal trajectories and provide practical collision avoidance manoeuvres. In particular, as the USV is underactuated and is restricted by various motion constraints, this paper has presented a new algorithm named the ‘angle-guidance fast marching square’ (AFMS), to make the generated path compliant with vehicle's dynamics and orientation restrictions. Based upon the AFMS, a formation path planning algorithm has been proposed to guide the USVs safely navigating in a cluttered environment. In addition, the formation forming problem has been specifically investigated with the algorithm being developed to make the USVs capable of forming the desired shape by following the trajectories from random initial configurations (positions and orientations). In order to eliminate the potential collision risks occurring on the route, a novel priority scheme based upon the distance to the closest point of approaching (DCPA) has also been proposed and developed. Algorithms have been validated on the computer-based simulations and are proven to work effectively in different environments
Path planning and collision avoidance for autonomous surface vehicles I: a review
Autonomous surface vehicles are gaining increasing attention worldwide due to the potential benefits of improving safety and efficiency. This has raised the interest in developing methods for path planning that can reduce the risk of collisions, groundings, and stranding accidents at sea, as well as costs and time expenditure. In this paper, we review guidance, and more specifically, path planning algorithms of autonomous surface vehicles and their classification. In particular, we highlight vessel autonomy, regulatory framework, guidance, navigation and control components, advances in the industry, and previous reviews in the field. In addition, we analyse the terminology used in the literature and attempt to clarify ambiguities in commonly used terms related to path planning. Finally, we summarise and discuss our findings and highlight the potential need for new regulations for autonomous surface vehicles
Collision Avoidance for Autonomous Surface Vessels using Novel Artificial Potential Fields
As the demand for transportation through waterways continues to rise, the
number of vessels plying the waters has correspondingly increased. This has
resulted in a greater number of accidents and collisions between ships, some of
which lead to significant loss of life and financial losses. Research has shown
that human error is a major factor responsible for such incidents. The maritime
industry is constantly exploring newer approaches to autonomy to mitigate this
issue. This study presents the use of novel Artificial Potential Fields (APFs)
to perform obstacle and collision avoidance in marine environments. This study
highlights the advantage of harmonic functions over traditional functions in
modeling potential fields. With a modification, the method is extended to
effectively avoid dynamic obstacles while adhering to COLREGs. Improved
performance is observed as compared to the traditional potential fields and
also against the popular velocity obstacle approach. A comprehensive
statistical analysis is also performed through Monte Carlo simulations in
different congested environments that emulate real traffic conditions to
demonstrate robustness of the approach.Comment: 28 pages, 30 figure
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