Beam Search Algorithm for Anti-Collision Trajectory Planning for Many-to-Many Encounter Situations with Autonomous Surface Vehicles (original) (raw)

Beam Search Algorithm for Ship Anti-Collision Trajectory Planning

Sensors

The biggest challenges in the maritime environment are accidents and excessive fuel consumption. In order to improve the safety of navigation at sea and to reduce fuel consumption, the strategy of anti-collision, shortest trajectory planning is proposed. The strategy described in this paper is based on the beam search method. The beam search algorithm (BSA) takes into account many safe trajectories for the present ship and chooses the best in terms of length and other criteria. The risk of collision of present ship with any target ships is detected when the closest point of approach (CPA) of the present ship is violated by the target ship’s planned trajectory. Only course alteration of the present ship is applied, and not speed alteration. The algorithm has been implemented in the decision support system NAVDEC and tested in a real navigation environment on the m/f Wolin, a Polish ferry. Almost all BSA trajectories calculated were shorter in comparison to the standard NAVDEC-calcula...

A Novel Method for Risk Assessment and Simulation of Collision Avoidance for Vessels based on AIS

The identification of risks associated with collision for vessels is an important element in maritime safety and management. A vessel collision avoidance system is a topic that has been deeply studied, and it is a specialization in navigation technology. The automatic identification system (AIS) has been used to support navigation, route estimation, collision prediction, and abnormal traffic detection. This article examined the main elements of ship collision, developed a mathematical model for the risk assessment, and simulated a collision assessment based on AIS information, thereby providing meaningful recommendations for crew training and a warning system, in conjunction with the AIS on board.

Toward Time-Optimal Trajectory Planning for Autonomous Ship Maneuvering in Close-Range Encounters

IEEE Journal of Oceanic Engineering, 2019

Ship intelligence has been a hot topic in recent years. How to achieve autonomous maneuvers in a complex marine environment in a safe, efficient, and low-cost manner is a fundamental task that ocean engineers face. This paper presents a two-stage trajectory planning scheme to address the minimumtime maneuvering problem in close-range encounters. The scheme is robust and versatile, as it can deal with the complex spatial variability, such as sea current, state constraints, marine traffic, and physical constraints, of close-range maneuvering. In the first stage, a directed graph with variable length is generated according to the sea current distribution. A wavefront search is applied on the graph to explore the reachability, the cost of state constraints, and the risk of collision. After a discrete solution has been found, the second stage involves searching for a smooth solution. A Bézier curve based parameter optimization approach is proposed to get rid of limited moving directions in the directed graph and explore around the discrete path. The result will be a near-optimal, smooth path. The proposed scheme has been tested to solve the Zermelo's ship steering problem and several other close-range maneuvering problems. The results demonstrate that the scheme is efficient in generating smoothed minimum-time trajectories for surface vessels when maneuvering in close-range encounters.

An AIS-Based Multiple Trajectory Prediction Approach for Collision Avoidance in Future Vessels

Volume 7B: Ocean Engineering

This study presents a method to predict the future trajectory of a target vessel using historical AIS data. The purpose of such a prediction is to aid in collision avoidance in future vessels. The method presented in this study extracts all trajectories present in an initial cluster centered about a vessel position. Features for each trajectory are then generated using Principle Component Analysis and used in clustering via unsupervised Gaussian mixture modeling. Each resultant cluster represents a possible future route the vessel may follow. A trajectory prediction is then conducted with respect to each cluster of trajectories discovered. This results in a prediction of multiple possible trajectories. The results indicate that the algorithm is effective in clustering the trajectories, where at least one cluster corresponds to the true trajectory of the vessel. The resultant predicted trajectories are also found to be reasonably accurate.

Review of Ship Collision Avoidance Guidance Algorithms Using Remote Sensing and Game Control

Remote Sensing

This work provides a mathematical description of a process game for the safe driving of a ship that encounters other ships. State and control constraint variables, as well as a set of acceptable ship tactics, are taken into consideration. Multi-criteria optimization operations are developed as positional and matrix games, based on cooperative, non-cooperative, and classical (non-game) optimal steering control. Adequate algorithms for ship collision avoidance, relating to the above operations, are developed and verified through digital simulation of a real navigational situation using MATLAB/Simulink.

A Method for Protocol-Based Collision Avoidance Between Autonomous Marine Surface Craft

This paper is concerned with the in-eld autonomous operation of unmanned marine vehicles in accordance with convention for safe and proper collision avoidance as prescribed by the Coast Guard Collision Regulations (COLREGS). These rules are written to train and guide safe human operation of marine vehicles and are heavily dependent on human common sense in determining rule applicability as well as rule execution, especially when multiple rules apply simultaneously. To capture the exibility exploited by humans, this work applies a novel method of multiobjective optimization, interval programming, in a behavior-based control framework for representing the navigation rules, as well as task behaviors, in a way that achieves simultaneous optimal satisfaction. We present experimental validation of this approach using multiple autonomous surface craft. This work represents the rst in-eld demonstration of multi-objective optimization applied to autonomous COLREGS-based marine vehicle navigation.

Swarming the Seas: Automated Collision Avoidance using Swarming Algorithms

SOME, 2018

With the increasing interest in the maritime industry in unmanned systems and maritime robotics, attention is shifting towards extremely safe automated collision avoidance systems (ACAS). An ACAS combines the sensors for situational awareness with specific algorithms that prevents a vessel from hitting other objects, usually other nearby vessels. The current state of technology is such that this is one of the big challenges for unmanned systems, as most ACAS are not sufficiently reliable to replace a human helmsman, especially in busy shipping lanes. In this paper we present an approach to ACAS which uses swarming algorithms to prevent vessels of colliding.Swarming algorithms have the characteristic that in itself simple – and therefore robust- rules for the individual participants in the swarm, can result in complex and intelligent behavior of the swarm. The hypothesis that was tested is that an individual ACAS will perform relatively poor in comparison to a solution where all the vessels within a certain range are made subject to a swarming strategy. In the simulations, this hypothesis was tested, not only for collision avoidance, but also with efficient routing and minimal mutual hindrance. The solution we propose is based on a layered approach, where steering actions are abstracted to a number of evasive strategies, on which machine learning can be applied. These strategies correspond to the rules that define the entire swarm. As a special measure of robustness, the swarm should be able to cater for nodes that do not conform to these rules, for instance floating objects or vessels that are not equipped with ACAS. A typical characteristic of a swarm algorithms is that all the participants in the swarm are equal. As vessels can be mutually different, we propose an additional layer of complexity where the models that the vessels make of the other participants are based on theories from evolutionary and social theories. The solution has been developed for the ship simulator of RH Marine on RDM Campus, and could be the onset for automated support for the helmsman, but with the eventual goal to develop ACAS for unmanned vessels. We have run extensive simulations with vessels in a shipping lane, where individual ACAS strategies were compared with a swarming solution.

A Mathematical Model for Analysis on Ships Collision Avoidance

This study develops a mathematical model for analysis on collision avoidance of ships. The obtained model provides information on the quantitative effect of the ship’s engine’s response and the applied reversing force on separation distance and stopping abilities of the ships. Appropriate evasive maneuvers require the engine’s ability to quickly reverse and an appropriate increment in the reversing force as proposed by the mathematical model. Numerical simulations were performed to demonstrate the validity of the mathematical model along with quantitative and qualitative analyses of the collision avoidance. It was found that the longer it takes the engine to move from forward to full astern, the longer the stopping distance and stopping time; and the closer the ships move, therefore increasing the risk of collision. However, the higher the amount of force applied in the opposite direction to the forward movement of the ships, the faster they will stop, reducing considerably risk of collision. This leads to the conclusion that for two encountering ships navigating in an area where alteration of course is not possible (confine water or no sufficient sea room) the increment in the ship resistance followed by an appropriate engine response are vital for imminent collision avoidance. Key words: Collision Avoidance, Mathematical Model, Numerical Simulations.

Optimal and game ship control algorithms for avoiding collisions at sea

Risk Analysis VI, 2008

This paper introduces the application of optimal and game theory methods in marine navigation. The functional scope of a standard ARPA anti-collision system ends with the simulation of the manoeuvre altering course or speed selected by the navigator. The problem of selecting such a manoeuvre is very difficult as the process of control is very complex and game making in its nature. The most adequate model of the process that has been adopted is a model of a differential game. The control goal is defined firstly, followed by a description of the base model and a presentation of approximated models. For each approximated model, an appropriate method of safe control to support the navigator decision in a collision situation has been assigned. The POSitional TRAJectory (POSTRAJ) and the RISK TRAJectory (RISKTRAJ) control algorithms have been designed. The considerations have been illustrated in examples of computer simulation algorithms to determine the safe ship trajectories in situations when passing many objects.