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

Jolanta Koszelew; Joanna Karbowska-Chilinska; Krzysztof Ostrowski; Piotr Kuczyński; Eric Kulbiej; Piotr Wołejsza

doi:10.3390/s20154115

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

Sensors ◽

10.3390/s20154115 ◽

2020 ◽

Vol 20 (15) ◽

pp. 4115

Author(s):

Jolanta Koszelew ◽

Joanna Karbowska-Chilinska ◽

Krzysztof Ostrowski ◽

Piotr Kuczyński ◽

Eric Kulbiej ◽

...

Keyword(s):

Trajectory Planning ◽

Search Algorithm ◽

Simulated Data ◽

General Idea ◽

Automatic Identification ◽

Identification System ◽

Beam Search ◽

Autonomous Surface Vehicles ◽

Open Sea ◽

Collision Trajectory

A single anti-collision trajectory generation problem for an “own” vessel only is significantly different from the challenge of generating a whole set of safe trajectories for multi-surface vehicle encounter situations in the open sea. Effective solutions for such problems are needed these days, as we are entering the era of autonomous ships. The article specifies the problem of anti-collision trajectory planning in many-to-many encounter situations. The proposed original multi-surface vehicle beam search algorithm (MBSA), based on the beam search strategy, solves the problem. The general idea of the MBSA involves the application of a solution for one-to-many encounter situations (using the beam search algorithm, BSA), which was tested on real automated radar plotting aid (ARPA) and automatic identification system (AIS) data. The test results for the MBSA were from simulated data, which are discussed in the final part. The article specifies the problem of anti-collision trajectory planning in many-to-many encounter situations involving moving autonomous surface vehicles, excluding Collision Regulations (COLREGs) and vehicle dynamics.

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Beam Search Algorithm for Ship Anti-Collision Trajectory Planning

Sensors ◽

10.3390/s19245338 ◽

2019 ◽

Vol 19 (24) ◽

pp. 5338 ◽

Cited By ~ 3

Author(s):

Joanna Karbowska-Chilinska ◽

Jolanta Koszelew ◽

Krzysztof Ostrowski ◽

Piotr Kuczynski ◽

Eric Kulbiej ◽

...

Keyword(s):

Decision Support ◽

Fuel Consumption ◽

Trajectory Planning ◽

Search Algorithm ◽

Search Method ◽

Reduce Fuel Consumption ◽

Beam Search ◽

Almost All ◽

Collision Trajectory ◽

Maritime Environment

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-calculated algorithm.

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Distributed Stochastic Search Algorithm for Multi-ship Encounter Situations

Journal of Navigation ◽

10.1017/s037346331700008x ◽

2017 ◽

Vol 70 (4) ◽

pp. 699-718 ◽

Cited By ~ 18

Author(s):

Donggyun Kim ◽

Katsutoshi Hirayama ◽

Tenda Okimoto

Keyword(s):

Distributed Algorithms ◽

Search Algorithm ◽

Real Data ◽

Stochastic Search ◽

Automatic Identification ◽

Identification System ◽

Local Search Algorithm ◽

Ship Collision ◽

Stochastic Search Algorithm ◽

Made In

Ship collision avoidance involves helping ships find routes that will best enable them to avoid a collision. When more than two ships encounter each other, the procedure becomes more complex since a slight change in course by one ship might affect the future decisions of the other ships. Two distributed algorithms have been developed in response to this problem: Distributed Local Search Algorithm (DLSA) and Distributed Tabu Search Algorithm (DTSA). Their common drawback is that it takes a relatively large number of messages for the ships to coordinate their actions. This could be fatal, especially in cases of emergency, where quick decisions should be made. In this paper, we introduce Distributed Stochastic Search Algorithm (DSSA), which allows each ship to change her intention in a stochastic manner immediately after receiving all of the intentions from the target ships. We also suggest a new cost function that considers both safety and efficiency in these distributed algorithms. We empirically show that DSSA requires many fewer messages for the benchmarks with four and 12 ships, and works properly for real data from the Automatic Identification System (AIS) in the Strait of Dover.

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Toward a Marine Road Network for Ship Passage Planning and Monitoring

Proceedings of the ICA ◽

10.5194/ica-proc-4-61-2021 ◽

2021 ◽

Vol 4 ◽

pp. 1-4

Author(s):

Sean M. Kohlbrenner ◽

Matthew K. Eager ◽

Nilan T. Phommachanh ◽

Christos Kastrisios ◽

Val Schmidt ◽

...

Keyword(s):

Global Economy ◽

Best Practice ◽

Route Planning ◽

Automatic Identification ◽

Identification System ◽

Safe Alternative ◽

Autonomous Surface Vehicles ◽

Map Generation ◽

Amazon Web Services ◽

Alternative Routes

Abstract. Safety of navigation is essential for the global economy as maritime trade accounts for more than 80% of international trade. Carrying goods by ship is economically and environmentally efficient, however, a maritime accident can cause harm to the environment and local economies. To ensure safe passage, mariners tend to use already familiar routes as a best practice; most groundings occur when a vessel travels in unfamiliar territories or suddenly changes its route, e.g., due to extreme weather. In highly trafficked areas, the highest risk for ships is that of collision with other vessels in the area. In these situations, a network of previously traversed routes could help mariners make informed decisions for finding safe alternative routes to the destination, whereas a system that can predict the routes of nearby vessels would ease the burden for the mariner and alleviate the risk of collision. The goal of this project is to utilize Automatic Identification System data to create a network of “roads” to promote a route planning and prediction system for ships that makes finding optimal routes easier and allows mariners on the bridge and Autonomous Surface Vehicles to predict movement of ships to avoid collisions. This paper presents the first steps taken toward this goal, including data processing through the usage of Python libraries, database design and development utilizing PostgreSQL, density map generation and visualizations through our own developed libraries, an A* pathfinding algorithm implementation, and an early implementation of an Amazon Web Services deployment.

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BCD Beam Search: considering suboptimal partial solutions in Bad Clade Deletion supertrees

PeerJ ◽

10.7717/peerj.4987 ◽

2018 ◽

Vol 6 ◽

pp. e4987

Author(s):

Markus Fleischauer ◽

Sebastian Böcker

Keyword(s):

Matrix Representation ◽

Search Algorithm ◽

Simulated Data ◽

Biological Data ◽

Beam Search ◽

Worst Case ◽

Running Time ◽

Minimum Cuts ◽

Supertree Methods

Supertree methods enable the reconstruction of large phylogenies. The supertree problem can be formalized in different ways in order to cope with contradictory information in the input. Some supertree methods are based on encoding the input trees in a matrix; other methods try to find minimum cuts in some graph. Recently, we introduced Bad Clade Deletion (BCD) supertrees which combines the graph-based computation of minimum cuts with optimizing a global objective function on the matrix representation of the input trees. The BCD supertree method has guaranteed polynomial running time and is very swift in practice. The quality of reconstructed supertrees was superior to matrix representation with parsimony (MRP) and usually on par with SuperFine for simulated data; but particularly for biological data, quality of BCD supertrees could not keep up with SuperFine supertrees. Here, we present a beam search extension for the BCD algorithm that keeps alive a constant number of partial solutions in each top-down iteration phase. The guaranteed worst-case running time of the new algorithm is still polynomial in the size of the input. We present an exact and a randomized subroutine to generate suboptimal partial solutions. Both beam search approaches consistently improve supertree quality on all evaluated datasets when keeping 25 suboptimal solutions alive. Supertree quality of the BCD Beam Search algorithm is on par with MRP and SuperFine even for biological data. This is the best performance of a polynomial-time supertree algorithm reported so far.

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Empirical Ship Domain based on AIS Data

Journal of Navigation ◽

10.1017/s0373463313000489 ◽

2013 ◽

Vol 66 (6) ◽

pp. 931-940 ◽

Cited By ~ 67

Author(s):

Martin Gamborg Hansen ◽

Toke Koldborg Jensen ◽

Tue Lehn-Schiøler ◽

Kristina Melchild ◽

Finn Mølsted Rasmussen ◽

...

Keyword(s):

Free Space ◽

Maximum Flow ◽

Automatic Identification ◽

Identification System ◽

Automatic Identification System ◽

Bridge Span ◽

Open Sea ◽

Flow Through

In this paper the minimum ship domain in which a navigator feels comfortable is estimated. That is, we estimate the free space surrounding a ship into which no other ship or object should enter. This is very useful when estimating the maximum flow through a channel or a bridge span. The paper benefits from the introduction of Automatic Identification System (AIS) data as it is now much easier to conduct studies involving a large number of observations. Our observations are based on ships sailing in southern Danish waters during a four year period, and from the observations we estimate how closely ships pass each other and fixed objects in open sea navigation. The main result is the establishment of an empirical minimum ship domain related to a comfortable navigational distance.

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