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Unmanned Surface Vehicle Collision Avoidance Path Planning in Restricted Waters Using Multi-Objective Optimisation Complying with COLREGs. SENSORS 2022; 22:s22155796. [PMID: 35957352 PMCID: PMC9371196 DOI: 10.3390/s22155796] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/29/2022] [Accepted: 07/31/2022] [Indexed: 11/19/2022]
Abstract
Navigation safety is one of the primary operational requirements for unmanned surface vehicles (USVs) in a complex marine environment, mainly guaranteed by a reliable path planning system for collision avoidance. This work proposes a novel weighted sum multi-objective optimisation strategy for USV collision avoidance path planning in restricted waters. In particular, the coefficients of different objectives could be tuned to emphasise the most critical design consideration under varying navigation scenarios. Moreover, in addition to the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), the terrain and weather constraints were also considered in the path planning system. The proposed USV collision avoidance path planning framework’s effectiveness was demonstrated through numerical simulations and hardware-in-the-loop (HIL) tests. The numerical simulation results indicate that the proposed method could avoid collision with dynamic and static obstacles, and it is also adaptive to different navigation restrictions and preferences. Moreover, a USV navigation platform was established by incorporating true Automatic Identification System (AIS) signals, and HIL tests were performed with real-time AIS data in a water channel in the Zhoushan archipelago. The results demonstrate that the proposed USV path planning strategy is applicable in restricted waters with complex terrains and weather constraints.
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Self-Organizing Cooperative Pursuit Strategy for Multi-USV with Dynamic Obstacle Ships. JOURNAL OF MARINE SCIENCE AND ENGINEERING 2022. [DOI: 10.3390/jmse10050562] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
A self-organizing cooperation strategy for multiple unmanned surface vessels (USVs) to pursue intelligent evaders in the case of a dynamic obstacle vessel is proposed. Firstly, a self-organizing cooperative hunting strategy is proposed to form an Apollonius circle. According to the escape strategies of evaders under different encirclement states, the pursuers are divided into pursuit group and ambush group. The pursuit group drives the evaders into the ambush area and completes the encirclement together with the ambush group. In order to better deal with the dynamic obstacle ships encountered in the pursuit process in the dynamic ocean environment, the artificial potential field-based collision avoidance method for inter-USV and dynamic collision avoidance strategy for encountering obstacle ships based on International Regulations for Preventing Collisions at Sea (COLREGs) are proposed. The simulation results show that the algorithm can make the pursuers complete the encirclement of the evaders and has good obstacle avoidance performance and flexibility in the environment with dynamic obstacle ships.
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Development of Priority Index for Intelligent Vessel Traffic Monitoring System in Vessel Traffic Service Areas. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12083807] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Recognizing dangerous situations in advance and determining priority is essential in vessel traffic surveillance. The traffic management priority is determined by the vessel traffic service operator (VTSO) employing the closest point of approach (CPA) and the time to CPA (TCPA) of the targets considering their current navigational data. Various environmental conditions influence CPA and TCPA, which affects the importance of surveillance. This study aims to support vessel traffic prioritization in the navigation surveillance of VTSO from the observer side. The vessel tracks were clustered based on density, and a priority index of the vessel surveillance was developed in the VTS area by reflecting regional navigation characteristics. Density-based spatial clustering of applications with noise (DBSCAN) was used for data clustering to classify the surveillance area. A fuzzy membership function was constructed based on the CPA and TCPA belonging to each cluster, and a dataset for determining priorities was constructed, yielding 17 clusters, fuzzy rules, and tables, with the priority index extracted for all vessel pairs to visualize the priority. The results indicated prior recognition of all dangerous situations. The proposed method facilitates vessel surveillance priority determination in high-density areas and predicts the risk in advance, thereby contributing to traffic management.
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Cho Y, Kim J, Kim J. Intent Inference-Based Ship Collision Avoidance in Encounters With Rule-Violating Vessels. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2021.3130386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Estimating Critical Latency Affecting Ship’s Collision in Re-Mote Maneuvering of Autonomous Ships. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112210987] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Estimation of the critical latency that can cause collision in remote maneuvering of autonomous ships can provide a clue to avoid collisions. The concept of estimating the critical latency was established using the turning circle formed by the turning maneuver of the own ship, and critical latency was estimated using the radius of the turning circle with the turning time ratio. The turning circle was observed using the turning trajectory of the give-way vessel measured in the ship maneuvering simulation experiment. Experimental results demonstrated that the proposed method is capable of identifying both the location and time of the collision due to critical latency. As a result, a clue to avoid possible collision in remote maneuvering caused by critical latency was deduced.
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COLREGs Compliant Fuzzy-Based Collision Avoidance System for Multiple Ship Encounters. JOURNAL OF MARINE SCIENCE AND ENGINEERING 2021. [DOI: 10.3390/jmse9080790] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
As the number of ships for marine transportation increases with the advancement of global trade, encountering multiple ships in marine traffic becomes common. This situation raises the risk of collision of the ships; hence, this paper proposes a novel Fuzzy-logic based intelligent conflict detection and resolution algorithm, where the collision courses and possible avoiding actions are analysed by considering ship motion dynamics and the input and output fuzzy membership functions are derived. As a conflict detection module, the Collision Risk (CR) is measured for each ship by using a scaled nondimensional Distance to the Closest Point of Approach (DCPA) and Time to the Closest Point of Approach (TCPA) as inputs. Afterwards, the decisions for collision avoidance are made based on the calculated CR, encountering angle and relative angle of each ship measured from others. In this regard, the rules for the Fuzzy interface system are defined in accordance with the COLREGs, and the whole system is implemented on the MATLAB Simulink platform. In addition, to deal with the multiple ship encounters, the paper proposes a unique maximum-course and minimum-speed change approach for decision making, which has been found to be efficient to solve Imazu problems, and other complicated multiple-ship encounters.
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Eriksen BOH, Bitar G, Breivik M, Lekkas AM. Hybrid Collision Avoidance for ASVs Compliant With COLREGs Rules 8 and 13-17. Front Robot AI 2021; 7:11. [PMID: 33501180 PMCID: PMC7805726 DOI: 10.3389/frobt.2020.00011] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Accepted: 01/20/2020] [Indexed: 11/23/2022] Open
Abstract
This paper presents a three-layered hybrid collision avoidance (COLAV) system for autonomous surface vehicles, compliant with rules 8 and 13–17 of the International Regulations for Preventing Collisions at Sea (COLREGs). The COLAV system consists of a high-level planner producing an energy-optimized trajectory, a model-predictive-control-based mid-level COLAV algorithm considering moving obstacles and the COLREGs, and the branching-course model predictive control algorithm for short-term COLAV handling emergency situations in accordance with the COLREGs. Previously developed algorithms by the authors are used for the high-level planner and short-term COLAV, while we in this paper further develop the mid-level algorithm to make it comply with COLREGs rules 13–17. This includes developing a state machine for classifying obstacle vessels using a combination of the geometrical situation, the distance and time to the closest point of approach (CPA) and a new CPA-like measure. The performance of the hybrid COLAV system is tested through numerical simulations for three scenarios representing a range of different challenges, including multi-obstacle situations with multiple simultaneously active COLREGs rules, and also obstacles ignoring the COLREGs. The COLAV system avoids collision in all the scenarios, and follows the energy-optimized trajectory when the obstacles do not interfere with it.
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Affiliation(s)
- Bjørn-Olav H Eriksen
- Department of Engineering Cybernetics, Centre for Autonomous Marine Operations and Systems, Norwegian University of Science and Technology, Trondheim, Norway
| | - Glenn Bitar
- Department of Engineering Cybernetics, Centre for Autonomous Marine Operations and Systems, Norwegian University of Science and Technology, Trondheim, Norway
| | - Morten Breivik
- Department of Engineering Cybernetics, Centre for Autonomous Marine Operations and Systems, Norwegian University of Science and Technology, Trondheim, Norway
| | - Anastasios M Lekkas
- Department of Engineering Cybernetics, Centre for Autonomous Marine Operations and Systems, Norwegian University of Science and Technology, Trondheim, Norway
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Liu J, Li H, Luo J, Xie S, Sun Y. Efficient obstacle detection based on prior estimation network and spatially constrained mixture model for unmanned surface vehicles. J FIELD ROBOT 2020. [DOI: 10.1002/rob.21983] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Jingyi Liu
- School of Mechatronic Engineering and Automation Shanghai University 99 Shangda Road Shanghai China
| | - Hengyu Li
- School of Mechatronic Engineering and Automation Shanghai University 99 Shangda Road Shanghai China
| | - Jun Luo
- School of Mechatronic Engineering and Automation Shanghai University 99 Shangda Road Shanghai China
| | - Shaorong Xie
- School of Mechatronic Engineering and Automation Shanghai University 99 Shangda Road Shanghai China
| | - Yu Sun
- Department of Mechanical and Industrial Engineering University of Toronto 5 King's College Road Toronto Ontario M5S 3G8 Canada
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A Novel Double Layered Hybrid Multi-Robot Framework for Guidance and Navigation of Unmanned Surface Vehicles in a Practical Maritime Environment. JOURNAL OF MARINE SCIENCE AND ENGINEERING 2020. [DOI: 10.3390/jmse8090624] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Formation control and cooperative motion planning are two major research areas currently being used in multi robot motion planning and coordination. The current study proposes a hybrid framework for guidance and navigation of swarm of unmanned surface vehicles (USVs) by combining the key characteristics of formation control and cooperative motion planning. In this framework, two layers of offline planning and online planning are integrated and applied on a practical marine environment. In offline planning, an optimal path is generated from a constrained A* path planning approach, which is later smoothed using a spline. This optimal trajectory is fed as an input for the online planning where virtual target (VT) based multi-agent guidance framework is used to navigate the swarm of USVs. This VT approach combined with a potential theory based swarm aggregation technique provides a robust methodology of global and local collision avoidance based on known positions of the USVs. The combined approach is evaluated with the different number of USVs to understand the effectiveness of the approach from the perspective of practicality, safety and robustness.
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Autonomous maritime collision avoidance: Field verification of autonomous surface vehicle behavior in challenging scenarios. J FIELD ROBOT 2020. [DOI: 10.1002/rob.21919] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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11
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Han J, Cho Y, Kim J, Kim J, Son N, Kim SY. Autonomous collision detection and avoidance for ARAGON USV: Development and field tests. J FIELD ROBOT 2020. [DOI: 10.1002/rob.21935] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Jungwook Han
- Maritime Safety and Environmental Research DivisionKRISODaejeon Republic of Korea
| | - Yonghoon Cho
- Department of Mechanical EngineeringKAISTDaejeon Republic of Korea
| | - Jonghwi Kim
- Department of Mechanical EngineeringKAISTDaejeon Republic of Korea
| | - Jinwhan Kim
- Department of Mechanical EngineeringKAISTDaejeon Republic of Korea
| | - Nam‐sun Son
- Maritime Safety and Environmental Research DivisionKRISODaejeon Republic of Korea
| | - Sun Young Kim
- Maritime Safety and Environmental Research DivisionKRISODaejeon Republic of Korea
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Eriksen BH, Breivik M, Wilthil EF, Flåten AL, Brekke EF. The branching‐course model predictive control algorithm for maritime collision avoidance. J FIELD ROBOT 2019. [DOI: 10.1002/rob.21900] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Bjørn‐Olav H. Eriksen
- Centre for Autonomous Marine Operations and Systems, Department of Engineering CyberneticsNorwegian University of Science and Technology (NTNU) Trondheim Norway
| | - Morten Breivik
- Centre for Autonomous Marine Operations and Systems, Department of Engineering CyberneticsNorwegian University of Science and Technology (NTNU) Trondheim Norway
| | - Erik F. Wilthil
- Centre for Autonomous Marine Operations and Systems, Department of Engineering CyberneticsNorwegian University of Science and Technology (NTNU) Trondheim Norway
| | - Andreas L. Flåten
- Centre for Autonomous Marine Operations and Systems, Department of Engineering CyberneticsNorwegian University of Science and Technology (NTNU) Trondheim Norway
| | - Edmund F. Brekke
- Centre for Autonomous Marine Operations and Systems, Department of Engineering CyberneticsNorwegian University of Science and Technology (NTNU) Trondheim Norway
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Thompson F, Guihen D. Review of mission planning for autonomous marine vehicle fleets. J FIELD ROBOT 2018. [DOI: 10.1002/rob.21819] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Fletcher Thompson
- National Centre for Maritime Engineering and Hydrodynamics; University of Tasmania; Tasmania Australia
| | - Damien Guihen
- National Centre for Maritime Engineering and Hydrodynamics; University of Tasmania; Tasmania Australia
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15
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Zhao Y, Li W, Shi P. A real-time collision avoidance learning system for Unmanned Surface Vessels. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.12.028] [Citation(s) in RCA: 109] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Shah BC, Švec P, Bertaska IR, Sinisterra AJ, Klinger W, von Ellenrieder K, Dhanak M, Gupta SK. Resolution-adaptive risk-aware trajectory planning for surface vehicles operating in congested civilian traffic. Auton Robots 2015. [DOI: 10.1007/s10514-015-9529-x] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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