490 research outputs found
COLREGs-Informed RRT* for Collision Avoidance of Marine Crafts
The paper proposes novel sampling strategies to compute the optimal path
alteration of a surface vessel sailing in close quarters. Such strategy
directly encodes the rules for safe navigation at sea, by exploiting the
concept of minimal ship domain to determine the compliant region where the path
deviation is to be generated. The sampling strategy is integrated within the
optimal rapidly-exploring random tree algorithm, which minimizes the length of
the path deviation. Further, the feasibility of the path with respect to the
steering characteristics of own ship is verified by ensuring that the position
of the new waypoints respects the minimum turning radius of the vessel. The
proposed sampling strategy brings a significant performance improvement both in
terms of optimal cost, computational speed and convergence rate.Comment: Accepted for publication at ICRA'2
Machine Learning for Enhanced Maritime Situation Awareness: Leveraging Historical AIS Data for Ship Trajectory Prediction
In this thesis, methods to support high level situation awareness in ship navigators through appropriate automation are investigated. Situation awareness relates to the perception of the environment (level 1), comprehension of the situation (level 2), and projection of future dynamics (level 3). Ship navigators likely conduct mental simulations of future ship traffic (level 3 projections), that facilitate proactive collision avoidance actions. Such actions may include minor speed and/or heading alterations that can prevent future close-encounter situations from arising, enhancing the overall safety of maritime operations.
Currently, there is limited automation support for level 3 projections, where the most common approaches utilize linear predictions based on constant speed and course values. Such approaches, however, are not capable of predicting more complex ship behavior. Ship navigators likely facilitate such predictions by developing models for level 3 situation awareness through experience. It is, therefore, suggested in this thesis to develop methods that emulate the development of high level human situation awareness. This is facilitated by leveraging machine learning, where navigational experience is artificially represented by historical AIS data.
First, methods are developed to emulate human situation awareness by developing categorization functions. In this manner, historical ship behavior is categorized to reflect distinct patterns. To facilitate this, machine learning is leveraged to generate meaningful representations of historical AIS trajectories, and discover clusters of specific behavior. Second, methods are developed to facilitate pattern matching of an observed trajectory segment to clusters of historical ship behavior. Finally, the research in this thesis presents methods to predict future ship behavior with respect to a given cluster. Such predictions are, furthermore, on a scale intended to support proactive collision avoidance actions.
Two main approaches are used to facilitate these functions. The first utilizes eigendecomposition-based approaches via locally extracted AIS trajectory segments. Anomaly detection is also facilitated via this approach in support of the outlined functions. The second utilizes deep learning-based approaches applied to regionally extracted trajectories. Both approaches are found to be successful in discovering clusters of specific ship behavior in relevant data sets, classifying a trajectory segment to a given cluster or clusters, as well as predicting the future behavior. Furthermore, the local ship behavior techniques can be trained to facilitate live predictions. The deep learning-based techniques, however, require significantly more training time. These models will, therefore, need to be pre-trained. Once trained, however, the deep learning models will facilitate almost instantaneous predictions
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
Safe Maneuvering Near Offshore Installations: A New Algorithmic Tool
Maneuvers of human-operated and autonomous marine vessels in the safety zone of drilling rigs, wind farms and other installations present a risk of collision. This article proposes an algorithmic toolkit that ensures maneuver safety, taking into account the restrictions imposed by ship dynamics. The algorithms can be used for anomaly detection, decision making by a human operator or an unmanned vehicle guidance system. We also consider a response to failures in the vessel's control systems and emergency escape maneuvers. Data used by the algorithms come from the vessel's dynamic positioning control system and positional survey charts of the marine installations
Real Time Motion Planning for Path Coverage with Applications in Ocean Surveying
Ocean surveying is the acquisition of acoustic data representing various features of the seafloor and the water above it, including water depth, seafloor composition, the presence of fish, and more. Historically, this was a task performed solely by manned vessels, but with advances in robotics and sensor technology, autonomous surface vehicles (ASVs) with sonar equipment are beginning to supplement and replace their more costly crewed counterparts. The popularity of these vessels calls for advances in software to control them.
In this thesis we define the problem of path coverage to represent and generalize that of ocean surveying, and propose a real-time motion planning algorithm to solve it. We prove theorems of completeness and local asymptotic optimality regarding the proposed algorithm, and evaluate it in a simulated environment. We also discover a lack of robustness in the Dubins vehicle model when applied to real-time motion planning. We implement a model-predictive controller and other components for an autonomous surveying system, and evaluate it in simulation. The system documented in this thesis takes a step towards fully autonomous ocean surveying, and proposes further extensions that get even closer to that goal
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