Advances in Autonomous Obstacle Avoidance for Unmanned Surface Vehicles

2007 ◽  
Author(s):  
Jacoby Larson ◽  
Michael Bruch ◽  
Ryan Halterman ◽  
John Rogers ◽  
Robert Webster
Keyword(s):  
Obstacle Avoidance ◽  
Unmanned Surface Vehicles

scholarly journals AutoTuning Environment for Static Obstacle Avoidance Methods Applied to USVs

2020 ◽  
Vol 8 (5) ◽  
pp. 300 ◽  
Author(s):  
Rafael Guardeño ◽  
Manuel J. López ◽  
Jesús Sánchez ◽  
Agustín Consegliere
Keyword(s):  
Mathematical Model ◽  
Evolutionary Algorithms ◽  
Numerical Simulations ◽  
Obstacle Avoidance ◽  
Laser Imaging ◽  
Unmanned Surface Vehicles ◽  
Sensor Model ◽  
Low Load ◽  
Different Types ◽  
Imaging Detection

This work is focused on reactive Static Obstacle Avoidance (SOA) methods used to increase the autonomy of Unmanned Surface Vehicles (USVs). Currently, there are multiple approaches to avoid obstacles, which can be applied to different types of USV. In order to assist in the choice of the SOA method for a particular vessel and to accelerate the pretuning process necessary for its implementation, this paper proposes a new AutoTuning Environment for Static Obstacle Avoidance (ATESOA) methods applied to USVs. In this environment, a new simplified modelling of a LIDAR (Laser Imaging Detection and Ranging) sensor is proposed based on numerical simulations. This sensor model provides a realistic environment for the tuning of SOA methods that, due to its low load computation, is used by evolutionary algorithms for the autotuning. In order to analyze the proposed ATESOA, three SOA methods were adapted and implemented to consider the measurements given by the LIDAR model. Furthermore, a mathematical model is proposed and evaluated for using as USV in the simulation enviroment. The results obtained in numerical simulations show how the new ATESOA is able to adjust the SOA methods in scenarios with different obstacle distributions.

scholarly journals Collision avoidance planning for unmanned surface vehicle based on eccentric expansion

2019 ◽  
Vol 16 (3) ◽  
pp. 172988141985194 ◽  
Author(s):  
Lifei Song ◽  
Zhuo Chen ◽  
Zaopeng Dong ◽  
Zuquan Xiang ◽  
Yunsheng Mao ◽  
...  
Keyword(s):  
Collision Avoidance ◽  
Obstacle Avoidance ◽  
Physical Simulation ◽  
Risk Level ◽  
Unmanned Surface Vehicle ◽  
Unmanned Surface Vehicles ◽  
Automatic Driving ◽  
International Regulations ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle

The International Regulations for Preventing Collisions at Sea (COLREGS) specify certain navigation rules for ships at risk for collision. Theoretically, the safety of unmanned surface vehicles and traffic boats would be guaranteed when they comply with the COLREGS. However, if traffic boats do not comply with the demands of the convention, thereby increasing the danger level, then adhering to the COLREGS may be dangerous for the unmanned surface vehicle. In this article, a dynamic obstacle avoidance algorithm for unmanned surface vehicles based on eccentric expansion was developed. This algorithm is used to solve the possible failure of collision avoidance when the unmanned surface vehicle invariably obeys the COLREGS during the avoidance process. An obstacle avoidance model based on the velocity obstacle method was established. Thereafter, an eccentric expansion operation on traffic boats was proposed to ensure a reasonable balance between safety and the rules of COLREGS. The expansion parameters were set according to the rules of COLREGS and the risk level of collision. Then, the collision avoidance parameters were calculated based on the aforementioned motion model. With the use of MATLAB and Unity software, a semi-physical simulation platform was established to perform the avoidance simulation experiment under different situations. Results show the validity, reliability and intellectuality of the algorithm. This research can be used for intelligent collision avoidance of unmanned surface vehicle and other automatic driving ships.

scholarly journals COLREGs: Compliant Dynamic Obstacle Avoidance of USVs Based on theDynamic Navigation Ship Domain

2021 ◽  
Vol 9 (8) ◽  
pp. 837
Author(s):  
Fang Deng ◽  
Leilei Jin ◽  
Xiuhui Hou ◽  
Longjin Wang ◽  
Boyang Li ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Collision Risk ◽  
Unmanned Surface Vehicles ◽  
Dynamic Navigation ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle ◽  
Path Planner ◽  
Trajectory Simulations

Dynamic obstacle avoidance is essential for unmanned surface vehicles (USVs) to achieve autonomous sailing. This paper presents a dynamic navigation ship domain (DNSD)-based dynamic obstacle avoidance approach for USVs in compliance with COLREGs. Based on the detected obstacle information, the approach can not only infer the collision risk, but also plan the local avoidance path trajectory to make appropriate avoidance maneuvers. Firstly, the analytical DNSD model is established taking into account the ship parameters, maneuverability, sailing speed, and encounter situations regarding COLREGs. Thus, the DNSDs of the own and target ships are utilized to trigger the obstacle avoidance mode and determine whether and when the USV should make avoidance maneuvers. Then, the local avoidance path planner generates the new avoidance waypoints and plans the avoidance trajectory. Simulations were implemented for a single obstacle under different encounter situations and multiple dynamic obstacles. The results demonstrated the effectiveness and superiority of the proposed DNSD-based obstacle avoidance algorithm.

A two-level dynamic obstacle avoidance algorithm for unmanned surface vehicles

2018 ◽  
Vol 170 ◽  
pp. 351-360 ◽  
Author(s):  
A. Lifei Song ◽  
B. Yiran Su ◽  
C. Zaopeng Dong ◽  
D. Wei Shen ◽  
E. Zuquan Xiang ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Unmanned Surface Vehicles ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle

A Research on Intelligent Obstacle Avoidance for Unmanned Surface Vehicles

2018 ◽  
Author(s):  
Tong Xinchi ◽  
Zhang Huajun ◽  
Chen Wenwen ◽  
Zhao Peimin ◽  
Leng Zhiwen ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Unmanned Surface Vehicles

A COLREGs-based obstacle avoidance approach for unmanned surface vehicles

2018 ◽  
Vol 169 ◽  
pp. 110-124 ◽  
Author(s):  
Yanlong Wang ◽  
Xuemin Yu ◽  
Xu Liang ◽  
Baoan Li
Keyword(s):  
Obstacle Avoidance ◽  
Unmanned Surface Vehicles

scholarly journals Autonomous Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on an Improved Velocity Obstacle Method

2021 ◽  
Vol 10 (9) ◽  
pp. 618
Author(s):  
Jia Ren ◽  
Jing Zhang ◽  
Yani Cui
Keyword(s):  
Collision Avoidance ◽  
Obstacle Avoidance ◽  
Collision Detection ◽  
Particle Swarm Algorithm ◽  
Unmanned Surface Vehicles ◽  
Detection Model ◽  
Planning Algorithm ◽  
Avoidance Strategy ◽  
Vessel Motion ◽  
Velocity Obstacle

Focusing on the collision avoidance problem for Unmanned Surface Vehicles (USVs) in the scenario of multi-vessel encounters, a USV autonomous obstacle avoidance algorithm based on the improved velocity obstacle method is proposed. The algorithm is composed of two parts: a multi-vessel encounter collision detection model and a path re-planning algorithm. The multi-vessel encounter collision detection model draws on the idea of the velocity obstacle method through the integration of characteristics such as the USV dynamic model in the marine environment, the encountering vessel motion model, and the International Regulations for Preventing Collisions at Sea (COLREGS) to obtain the velocity obstacle region in the scenario of USV and multi-vessel encounters. On this basis, two constraint conditions for the motion state space of USV obstacle avoidance behavior and the velocity obstacle region are added to the dynamic window algorithm to complete a USV collision risk assessment and generate a collision avoidance strategy set. The path re-planning algorithm is based on the premise of the minimum resource cost and uses an improved particle swarm algorithm to obtain the optimal USV control strategy in the collision avoidance strategy set and complete USV path re-planning. Simulation results show that the algorithm can enable USVs to safely evade multiple short-range dynamic targets under COLREGS.

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