Developing a navigation, guidance and obstacle avoidance algorithm for an Unmanned Surface Vehicle (USV) by algorithms fusion

Mousazadeh, Hossein; Jafarbiglu, Hamid; Abdolmaleki, Hamid; Omrani, Elham; Monhaseri, Farshid; Abdollahzadeh, Mohammadreza; Mohammadi-Aghdam, Aref; Kiapei, Ali; Salmani-Zakaria, Yousef; Makhsoos, Ashkan

doi:10.1016/j.oceaneng.2018.04.018

Cited by 128 publications

(77 citation statements)

References 18 publications

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“…Unlike manned ships, the majority of unmanned surface vehicles are not equipped with AIS systems. USVs usually work with radars, sonars and machine vision to obtain the azimuth, the distance as well as the quantity of ships and obstacles nearby [46]. In order to build a universal model of the obstacle extension function, we took the length and breadth of the USV and the number of obstacles in multiple bearings as parameters, and established the sub-extension function ζ(Ω NoB ).…”

Section: Numbers Of Obstaclesmentioning

confidence: 99%

A COLREGs-Based Dynamic Navigation Safety Domain for Unmanned Surface Vehicles: A Case Study of Dolphin-I

Zhou

Wang

Zhang

2020

JMSE

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Unmanned Surface Vehicles (USVs) are intelligent machines that have been widely studied in recent years. The safety of USVs’ activities is a priority issue in their applications; one effective method is to delimit an exclusive safety domain around the USV. Besides considering collision avoidance, the safety domain should satisfy the requirements of encounter situations in the COLREGs (International Regulations for Preventing Collisions at Sea) as well. Whereas the model providing the safety domain for the USVs is defined through the experience of the manned ships, a specific model for USVs has been rarely studied. A dynamic navigation safety domain (DNSD) for USVs was proposed in this paper. To construct the model, the essential factors that could affect the navigation safety of the USVs were extracted via a rough set, and the extension functions of these factors were carried out. The DNSD was employed in various situations and compared with the ship domain models of common ships. It was found that the domain boundary can be automatically corrected according to the change in the working conditions when the DNSD is in use. Compared with the Fujii and Coldwell models, the DNSD can provide a larger safety area for a USV’s action of collision avoidance.

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Section: Numbers Of Obstaclesmentioning

confidence: 99%

A COLREGs-Based Dynamic Navigation Safety Domain for Unmanned Surface Vehicles: A Case Study of Dolphin-I

Zhou

Wang

Zhang

2020

JMSE

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“…Undoubtedly, high speed is the primal index to evaluate the performance, while it may also introduce new difficulties like reliable path planning, dynamic constraints and energy saving. 9 The higher speed definitely improves penetrability, work range and efficiency, which totally enhances the utility of USV. Therefore, a secure and feasible path is necessary to serve high-speed USV.…”

Section: Introductionmentioning

confidence: 99%

Path planning for unmanned surface vehicle based on predictive artificial potential field

Hao

2020

International Journal of Advanced Robotic Systems

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The path planning for high-speed unmanned surface vehicle raises more complicated requirements to reduce sailing time and save energy. In this article, a new predictive artificial potential field is proposed using time information and predictive potential to plan a smoother path. The principle of artificial potential field with vehicle dynamics and reachability in local minimum is studied. According to global and local path planning, the most state-of-the-art traditional artificial potential field and its drawback are analysed at first. Then we proposed predictive artificial potential field with three modifications: angle limit, velocity adjustment and predictive potential to improve the feasibility and flatness of the generated path. In addition, we compare the performance between traditional artificial potential field and predictive artificial potential field, where predictive artificial potential field successfully restricts the maximum turning angle, cuts short sailing time and intelligently avoids obstacle. From the simulation results, we also verify that predictive artificial potential field can solve concave local minimum problem and enhance the reachability in special scenario. Therefore, the more reasonable path generated by predictive artificial potential field reduces sailing time and helps conserve more energy for unmanned surface vehicle.

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“…The obstacle size and velocity vector of the unmanned surface vehicles can be used to achieve the obstacle avoidance. Moreover, fusion of more than one navigation algorithms can result more accurate outcomes [3]. Various requirements of obstacle avoidance can be satisfied for the changing distance between robot and the obstacles [4].…”

Section: Introductionmentioning

confidence: 99%

Robot navigation in unknown environment with obstacle recognition using laser sensor

Kumar

Vamossy

2019

IJECE

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<p>Robot navigation in unknown and dynamic environments may result in aimless wandering, corner traps and repetitive path loops. To address these issues, this paper presents the solution by comparing the standard deviation of the distance ranges of the obstacles appeared in the robot navigation path. For the similar obstacles, The standard deviations of distance range vectors, obtained from the laser range finder sensor of the robot at similar pose, are very close to each other. Therefore, the measurements of odometer sensor are also combined with the standard deviation to recognize the location of the obstacles. A novel algorithm, with obstacle detection feature, is presented for robot navigation in unknown and dynamic environments. The algorithm checks the similarity of the distance range vectors of the obstacles in the path and uses this information in combination with the odometer measurements to identify the obstacles and their locations. The experimental work is carried out using Gazebo simulator.</p>

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Developing a navigation, guidance and obstacle avoidance algorithm for an Unmanned Surface Vehicle (USV) by algorithms fusion

Cited by 128 publications

References 18 publications

A COLREGs-Based Dynamic Navigation Safety Domain for Unmanned Surface Vehicles: A Case Study of Dolphin-I

A COLREGs-Based Dynamic Navigation Safety Domain for Unmanned Surface Vehicles: A Case Study of Dolphin-I

Path planning for unmanned surface vehicle based on predictive artificial potential field

Robot navigation in unknown environment with obstacle recognition using laser sensor

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