Real-time Autonomous UAV Formation Flight with Collision and Obstacle Avoidance in Unknown Environment

Çetin, Ömer; Yılmaz, Güray

doi:10.1007/s10846-015-0318-8

Cited by 59 publications

(34 citation statements)

References 16 publications

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“…A pre-planned 3d distribution of multiple uavs is performed to optimize power used by each aircraft in [2]. Similar approaches to control uavs include partially autonomous uavs that need periodic interactions with a centralized (often ground-based) controller to eliminate long-distance communication links in [5] and costly image processing and coordination procedures running in uavs designed for tasks such as topology formation and obstacle avoidance [6]. In [48], uavs require coordination procedures and target state sharing in their ant colony based task allocation mechanism.…”

Section: Swarms Of Uavsmentioning

confidence: 99%

Artificial intelligence and game theory controlled autonomous UAV swarms

Kusyk

Samoylov

et al. 2020

Evol. Intel.

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Autonomous unmanned aerial vehicles (uavs) operating as a swarm can be deployed in austere environments, where cyber electromagnetic activities often require speedy and dynamic adjustments to swarm operations. Use of central controllers, uav synchronization mechanisms or pre-planned set of actions to control a swarm in such deployments would hinder its ability to deliver expected services. We introduce artificial intelligence and game theory based flight control algorithms to be run by each autonomous uav to determine its actions in near real-time, while relying only on local spatial, temporal and electromagnetic (em) information. Each uav using our flight control algorithms positions itself such that the swarm maintains mobile ad-hoc network (manet) connectivity and uniform asset distribution over an area of interest. Typical tasks for swarms using our algorithms include detection, localization and tracking of mobile em transmitters. We present a formal analysis showing that our algorithms can guide a swarm to maintain a connected manet, promote a uniform network spreading, while avoiding overcrowding with other swarm members. We also prove that they maintain manet connectivity and, at the same time, they can lead a swarm of autonomous uavs to follow or avoid an em transmitter. Simulation experiments in opnet modeler verify the results of formal analysis that our algorithms are capable of providing an adequate area coverage over a mobile em source and maintain manet connectivity. These algorithms are good candidates for civilian and military applications that require agile responses to the changes in dynamic environments for tasks such as detection, localization and tracking mobile em transmitters.

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Section: Swarms Of Uavsmentioning

confidence: 99%

Artificial intelligence and game theory controlled autonomous UAV swarms

Kusyk

Samoylov

et al. 2020

Evol. Intel.

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“…However, research on distributed control drones is often used in fixed-wing drones to study how to make the formation well maintained in high-speed movement and achieve rapid convergence after avoiding obstacles [8][9][10]; and multirotor UAV formations often use centralized control to plan flight routes in advance according to the expected formation. It is the high-precision positioning based on visual and advanced communication technologies that often determines the formation complexity and completion [11,12].…”

Section: Development Statusmentioning

confidence: 99%

Study on Composing Dense Formations in a Dynamic Environment of Multirotor UAVs by Distributed Control

Zhou

Shen

Wang

et al. 2018

Mathematical Problems in Engineering

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In order to make multirotor unmanned aerial vehicles (UAV) compose a desired dense formation and improve the practicality of UAV formation, a distributed algorithm based on fuzzy logic was proposed. The airflow created by multirotor UAVs was analyzed according to the structure of the multirotor UAV and the characteristic equation of the fluid. This paper presented a dynamic model for the process of formation of and path search algorithm based on this model. The membership function in this model combines the factors of position, flow field, and movement. Integrating the dynamic model and its desired position in formations, each UAV evaluates the surrounding points and then selects the direction for step motion. Through simulation, this algorithm was improved by a by-step formation approach, and the effectiveness of this method in dense formation of multirotor UAVs was proved.

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“…Cooperation of multiple intelligent units in wireless networks (known as multiagent systems) has attracted considerable attention in the recent decades. Numerous research has offered control algorithms for various collective behavior of multiagent systems, for example, consensus [21][22][23][24] and formation [25][26][27], to achieve diverse functions like tracking [22,23], coverage [28,29], surrounding [30], and so forth [31]. The key principle of multiagent cooperation is that each agent pursues its own local goal and that the global goal emerges from the interaction of agents [32].…”

Section: Related Workmentioning

confidence: 99%

“…A Ushaped valley or the ones having corners and traps on the borders are particular cases of the main problem, and they can be solved by combining the proposed scheme with navigation and obstacle avoidance algorithms. To be specific, the UAVs can change the main direction (the positive -axis) during the coverage motion according to navigation methods offered in [31,39] for U-turn environment and apply obstacle avoidance strategy provided by [25,26] when encountering corners and traps on the borders, which can be taken as obstacles.…”

Section: Solutions To Special Cases With Validation Simulationsmentioning

confidence: 99%

Distributed Control of Networked Unmanned Aerial Vehicles for Valley Area Coverage

Shi

Qin

2016

Mobile Information Systems

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The paper provides a novel cooperative motion scheme for networked Unmanned Aerial Vehicles (UAVs) to fully sweep-cover a priori unknown elongated areas with curved borders, which are termed “valley areas.” The UAVs’ motion is confined between the borders. Different from former research on straight-corridor-sweep-coverage, in each valley area, the width of different portions varies dramatically: the UAVs need to line up across the valley area to achieve full coverage of the widest portions while they can only pass through the narrowest parts one by one in a queue. The UAVs are provided with barrier detection and inter-UAV communication. According to the scheme, a distributed control law has been offered for discrete-time multi-UAV systems, guaranteeing crash avoidance and full coverage while considering the constrained mobility of the UAVs. Regular and extreme simulations are carried out to verify the efficacy and stability of the proposed algorithm. Solutions to U-shaped valley coverage and the case of insufficient UAVs available are discussed with validation simulations. Comparison simulations are conducted with respect to a line-sweep-coverage algorithm developed by a closely related work, and differences in performance are revealed subsequently. Conclusions are drawn with possible directions of future research.

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Real-time Autonomous UAV Formation Flight with Collision and Obstacle Avoidance in Unknown Environment

Cited by 59 publications

References 16 publications

Artificial intelligence and game theory controlled autonomous UAV swarms

Artificial intelligence and game theory controlled autonomous UAV swarms

Study on Composing Dense Formations in a Dynamic Environment of Multirotor UAVs by Distributed Control

Distributed Control of Networked Unmanned Aerial Vehicles for Valley Area Coverage

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