A Rule-based Heuristic Method for COLREGS-compliant Collision Avoidance for an Unmanned Surface Vehicle

2012 ◽  
Vol 45 (27) ◽  
pp. 386-391 ◽  
Author(s):  
S. Campbell ◽  
W. Naeem
Keyword(s):  
Collision Avoidance ◽  
Heuristic Method ◽  
Unmanned Surface Vehicle ◽  
Rule Based

scholarly journals Collision Avoidance of Podded Propulsion Unmanned Surface Vehicle With COLREGs Compliance and Its Modeling and Identification

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 55473-55491 ◽  
Author(s):  
Xiaojie Sun ◽  
Guofeng Wang ◽  
Yunsheng Fan ◽  
Dongdong Mu ◽  
Bingbing Qiu
Keyword(s):  
Collision Avoidance ◽  
Unmanned Surface Vehicle

scholarly journals Deep Learning in Unmanned Surface Vehicles Collision-Avoidance Pattern Based on AIS Big Data with Double GRU-RNN

2020 ◽  
Vol 8 (9) ◽  
pp. 682
Author(s):  
Jia-hui Shi ◽  
Zheng-jiang Liu
Keyword(s):  
Big Data ◽  
Collision Avoidance ◽  
Difficult Problem ◽  
Automatic Identification ◽  
Identification System ◽  
Unmanned Surface Vehicle ◽  
Effective Learning ◽  
Maritime Navigation ◽  
Ship Collision ◽  
Gated Recurrent Unit

There is a collection of a large amount of automatic identification system (AIS) data that contains ship encounter information, but mining the collision avoidance knowledge from AIS big data and carrying out effective machine learning is a difficult problem in current maritime field. Herein, first the Douglas–Peucker (DP) algorithm was used to preprocess the AIS data. Then, based on the ship domain the risk of collision was identified. Finally, a double-gated recurrent unit neural network (GRU-RNN) was constructed to learn unmanned surface vehicle (USV) collision avoidance decision from the extracted data of successful encounters of ships. The double GRU-RNN was trained on the 2015 Tianjin Port AIS dataset to realize the effective learning of ship encounter data. The results indicated that the double GRU-RNN could effectively learn the collision avoidance pattern hidden in AIS big data, and generate corresponding ship collision-avoidance decisions for different maritime navigation states. This study contributes significantly to the increased efficiency and safety of sea operations. The proposed method could be potentially applied to USV technology and intelligence collision avoidance.

scholarly journals An autonomous dynamic collision avoidance control method for unmanned surface vehicle in unknown ocean environment

2019 ◽  
Vol 16 (2) ◽  
pp. 172988141983158 ◽  
Author(s):  
Yunsheng Fan ◽  
Xiaojie Sun ◽  
Guofeng Wang
Keyword(s):  
Collision Avoidance ◽  
Autonomous Navigation ◽  
Control Method ◽  
Unmanned Surface Vehicle ◽  
Time Dynamic ◽  
Collision Avoidance Control ◽  
Dynamic Obstacle Avoidance ◽  
Avoidance Control ◽  
Dynamic Obstacle ◽  
Ocean Environment

There are many unknown obstacles in the sea, so the autonomous navigation of unmanned surface vehicle needs to avoid them as soon as possible even under the condition of low control ability of the controller. To solve the problem, by combining the dynamic collision avoidance algorithm and tracking control, a dynamic collision avoidance control method in the unknown ocean environment is presented. In this article, in consideration of the unknown ocean environment and real-time dynamic obstacle avoidance problem, the collision avoidance controller using a velocity resolution method and backstepping tracking controller based on unmanned surface vehicle maneuvering motion model is designed. Simulation results show that the method is effective and accurate and can provide the reference for the unmanned surface vehicle intelligent collision avoidance control technology.

scholarly journals Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Guoqing Xia ◽  
Zhiwei Han ◽  
Bo Zhao ◽  
Xinwei Wang
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Optimization Problem ◽  
Quantum Particle ◽  
Swarm Optimization ◽  
Unmanned Surface Vehicle ◽  
Kinematic Constraints ◽  
Quantum Particle Swarm Optimization ◽  
Local Path Planning ◽  
Local Path

An unmanned surface vehicle (USV) plans its global path before the mission starts. When dynamic obstacles appear during sailing, the planned global path must be adjusted locally to avoid collision. This study proposes a local path planning algorithm based on the velocity obstacle (VO) method and modified quantum particle swarm optimization (MQPSO) for USV collision avoidance. The collision avoidance model based on VO not only considers the velocity and course of the USV but also handles the variable velocity and course of an obstacle. According to the collision avoidance model, the USV needs to adjust its velocity and course simultaneously to avoid collision. Due to the kinematic constraints of the USV, the velocity window and course window of the USV are determined by the dynamic window approach (DWA). In summary, local path planning is transformed into a multiobjective optimization problem with multiple constraints in a continuous search space. The optimization problem is to obtain the USV’s optimal velocity variation and course variation to avoid collision and minimize its energy consumption under the rules of the International Regulations for Preventing Collisions at Sea (COLREGs) and the kinematic constraints of the USV. Since USV local path planning is completed in a short time, it is essential that the optimization algorithm can quickly obtain the optimal value. MQPSO is primarily proposed to meet that requirement. In MQPSO, the efficiency of quantum encoding in quantum computing and the optimization ability of representing the motion states of the particles with wave functions to cover the whole feasible solution space are combined. Simulation results show that the proposed algorithm can obtain the optimal values of the benchmark functions and effectively plan a collision-free path for a USV.

scholarly journals COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle Using Deep Reinforcement Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 165344-165364
Author(s):  
Eivind Meyer ◽  
Amalie Heiberg ◽  
Adil Rasheed ◽  
Omer San
Keyword(s):  
Reinforcement Learning ◽  
Collision Avoidance ◽  
Unmanned Surface Vehicle
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