2020
DOI: 10.1109/access.2020.3000064
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Abstract: Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). A lot of work is being done to make the CAS as safe and reliable as possible, necessitating a comparative study of the recent work in this important area. The paper provides a comprehensive review of collision avoidance strategies used for unmanned vehicles, with the main emphasis on unmanned aerial vehicles (UAV). It is an in-depth survey of different collision avoidance techniques that are categoric… Show more

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Cited by 166 publications
(80 citation statements)
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References 125 publications
(126 reference statements)
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“…During the flight, they can encounter both stationary and moving obstacles and objects that need to be safely and reliably evaded using the collision avoidance system [23], [24]. Typically, algorithms for collision avoidance can be divided into three generic classes [25], [26]: 1) force-field methods that work on the principle of applying attractive/repulsive electric forces existing amongst charged objects; each drone in a swarm is considered a charged particle, and attractive or repulsive forces between drones and the obstacles are used to generate and choose the routes to be taken [27], [28]; 2) sense-andavoid based methods, where the process of collision avoidance is simplified into individual detection and avoidance of the objects and obstacles, resulting in short response times and reducing the computational power needed [29], [30]; and 3) optimization based methods which focus on providing the optimal or near-optimal solutions for path planning and motion characteristics of each drone with respect to the other drones and obstacles. In order to calculate efficient routes within a finite time horizon, these methods rely on static objects with known locations and dimensions [31], [32].…”
Section: Related Workmentioning
confidence: 99%
“…During the flight, they can encounter both stationary and moving obstacles and objects that need to be safely and reliably evaded using the collision avoidance system [23], [24]. Typically, algorithms for collision avoidance can be divided into three generic classes [25], [26]: 1) force-field methods that work on the principle of applying attractive/repulsive electric forces existing amongst charged objects; each drone in a swarm is considered a charged particle, and attractive or repulsive forces between drones and the obstacles are used to generate and choose the routes to be taken [27], [28]; 2) sense-andavoid based methods, where the process of collision avoidance is simplified into individual detection and avoidance of the objects and obstacles, resulting in short response times and reducing the computational power needed [29], [30]; and 3) optimization based methods which focus on providing the optimal or near-optimal solutions for path planning and motion characteristics of each drone with respect to the other drones and obstacles. In order to calculate efficient routes within a finite time horizon, these methods rely on static objects with known locations and dimensions [31], [32].…”
Section: Related Workmentioning
confidence: 99%
“…In recent years, with the rapid development of Artificial Intelligence (AI) technology [1,2], many intelligent algorithms, such as genetic algorithms, expert intelligence systems [3,4], neural network algorithms [5][6][7], and fuzzy logic algorithms [8,9] are widely used in the automation research of automobiles and aerial vehicle [10]. On the other side, the revolution of navigation technology also promoted the development of ship automation and ship intelligence technology, and the Maritime Autonomous Surface Ships (MASS) or the Unmanned Surface Vehicles (USV) have become a popular research topic [11][12][13].…”
Section: Introductionmentioning
confidence: 99%
“…Bearing in mind the considerably low risk to human life, as well as improved durability for longer missions and accessibility in difficult terrains, the demand for such unmanned vehicles is increasing rapidly and their path planning in dynamic environments remains one of the most challenging issues to solve [6]. Due to their autonomy and ability to travel far from the base stations or their operators (the range naturally depends on the type and size of the vehicle), the need for having an onboard mechanism to avoid collisions with objects and other vehicles is obvious [7][8][9][10].…”
Section: Introductionmentioning
confidence: 99%