Collision avoidance of multi unmanned aerial vehicles: A review

Huang, Sunan; Teo, Rodney; Tan, Kok Kiong

doi:10.1016/j.arcontrol.2019.10.001

Cited by 138 publications

(53 citation statements)

References 58 publications

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“…The robotics community is striving hard to address these challenges and to bring the technological level suited for the demanding environments ensuring success and safe navigation of the unmanned vehicles [30]- [32]. Obstacle detection and collision avoidance are one of the most challenging issues for autonomous vehicles and become even more critical in dynamic environments with multiple UAVs and moving obstacles [33]- [35].…”

Section: Figure 2: Detection Range and Collision Radiusmentioning

confidence: 99%

Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

et al. 2020

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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 categorically explained along with a comparative analysis of the considered approaches w.r.t. different scenarios and technical aspects. This also includes a discussion on the use of different types of sensors for collision avoidance in the context of UAVs. INDEX TERMS autonomous aerial vehicles, autonomous vehicles, collision avoidance, active and passive sensors, optimisation-based, force-field based, sense and avoid, geometry based

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Section: Figure 2: Detection Range and Collision Radiusmentioning

confidence: 99%

Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

et al. 2020

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“…The resultant gait contains both the function of recent or current reading of the robot sensors and also the goal position or its relative location to the goal position. An indoor mobile robot comprises various functions, of which avoiding obstacles is a vital part, which is a must to keep overall monitoring of the architecture for maneuvering tasks [256].…”

Section: E Obstacle Avoidancementioning

confidence: 99%

Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

et al. 2021

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Robots that can move autonomously and can make intelligent decisions by perceiving their environments and surrounding objects are known as autonomous mobile robots. Such robots have rapidly moved from laboratories to automated industries to fill a variety of roles in our lives, homes, offices, hospitals, industries, and even on the streets. The interest in mobile robots is growing rapidly, prompting an enormous amount of research over the last 30 years, on critical factors of mobile robots such as locomotion, perception, localization, mapping, ego-motion tracking, and dynamic navigation. This article surveys these essential factors of autonomous mobile robots in terms of mathematical modeling, control issues, and challenging factors. Brief discussions are provided on the fundamentals of these technologies, popular algorithms in comprehensive mode, future challenges, and promising directions to guide the construction of an autonomous mobile robot with high accuracy and effectiveness. Since it is difficult to find complete coverage of those topics in a single location, this article provides a guideline for researchers entering the field or for innovators in the mobile robotics sector. The paper also examines open challenges in indoor mobile robots and identifies potential futures for autonomous mobile robots.

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“…It can realize realtime obstacle avoidance but cannot resolve complex conflict problems. In [18][19][20], the speed obstacle method broadcasts automatic correlation monitoring to give each UAV the position and speed of the others; thus it solved the potential problem by detecting flight conflict and determining a relief flight path; however, the relief path can easily deviate the UAV from the search target point, thus compromising mission efficiency. In [21], the distributed model predictive control method is adopted, in which the collision avoidance management unit and the interactive graph updating mechanism address conflict resolution in multi-UAV route planning, but it requires a large amount of computation.…”

Section: Interactive Decision Functionmentioning

confidence: 99%

“…(1) It designed a cooperative search method suitable for dynamic communication distance (2) It analyzed the prior information of four typical moving targets to generate a mathematical model that defines them in a cooperative search (3) It provided an interface to apply to current UAV conflict resolution results [18][19][20][21] in swarm cooperative search missions…”

Section: Introductionmentioning

confidence: 99%

Cooperative Target Search of UAV Swarm with Communication Distance Constraint

Wang

Liang

et al. 2021

Mathematical Problems in Engineering

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This paper proposes a cooperative search algorithm to enable swarms of unmanned aerial vehicles (UAVs) to capture moving targets. It is based on prior information and target probability constrained by inter-UAV distance for safety and communication. First, a rasterized environmental cognitive map is created to characterize the task area. Second, based on Bayesian theory, the posterior probability of a target’s existence is updated using UAV detection information. Third, the predicted probability distribution of the dynamic time-sensitive target is obtained by calculating the target transition probability. Fourth, a customized information interaction mechanism switches the interaction strategy and content according to the communication distance to produce cooperative decision-making in the UAV swarm. Finally, rolling-time domain optimization generates interactive information, so interactive behavior and autonomous decision-making among the swarm members are realized. Simulation results showed that the proposed algorithm can effectively complete a cooperative moving-target search when constrained by communication distance yet still cooperate effectively in unexpected situations such as a fire.

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Collision avoidance of multi unmanned aerial vehicles: A review

Cited by 138 publications

References 58 publications

Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

Cooperative Target Search of UAV Swarm with Communication Distance Constraint

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