Avoidance maps: A new concept in UAV collision avoidance

Tony, Lima Agnel; Ghose, Debasish; Chakravarthy, Animesh

doi:10.1109/icuas.2017.7991382

Cited by 20 publications

(8 citation statements)

References 17 publications

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“…Meanwhile, Constraints (17) - (20) ensure that each UAV should start from the initial point and stop at the end point. Last but not least, constraint (21) ensures that a UAV's velocity should not exceed its maximum allowed velocity. Mainly, this model aims to seek for the optimal velocity and direction by finding out (X k (t), Y k (t)) that help in reducing the traveled distance while preventing collisions among UAVs.…”

Section: B Detailed Solution Descriptionmentioning

confidence: 99%

Energy and Delay Aware Physical Collision Avoidance in Unmanned Aerial Vehicles

Ouahouah¹,

Prados-Garzon²,

Taleb³

et al. 2018

2018 IEEE Global Communications Conference (GLOBECOM)

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Several solutions have been proposed in the literature to address the Unmanned Aerial Vehicles (UAVs) collision avoidance problem. Most of these solutions consider that the ground controller system (GCS) determines the path of a UAV before starting a particular mission at hand. Furthermore, these solutions expect the occurrence of collisions based only on the GPS localization of UAVs as well as via object-detecting sensors placed on board UAVs. The sensors' sensitivity to environmental disturbances and the UAVs' influence on their accuracy impact negatively the efficiency of these solutions. In this vein, this paper proposes a new energy-and delay-aware physical collision avoidance solution for UAVs. The solution is dubbed EDC-UAV. The primary goal of EDC-UAV is to build in-flight safe UAVs trajectories while minimizing the energy consumption and response time. We assume that each UAV is equipped with a global positioning system (GPS) sensor to identify its position. Moreover, we take into account the margin error of the GPS to provide the position of a given UAV. The location of each UAV is gathered by a cluster head, which is the UAV that has either the highest autonomy or the greatest computational capacity. The cluster head runs the EDC-UAV algorithm to control the rest of the UAVs, thus guaranteeing a collisionfree mission and minimizing the energy consumption to achieve different purposes. The proper operation of our solution is validated through simulations. The obtained results demonstrate the efficiency of EDC-UAV in achieving its design goals.

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Section: B Detailed Solution Descriptionmentioning

confidence: 99%

Energy and Delay Aware Physical Collision Avoidance in Unmanned Aerial Vehicles

Ouahouah¹,

Prados-Garzon²,

Taleb³

et al. 2018

2018 IEEE Global Communications Conference (GLOBECOM)

View full text Add to dashboard Cite

show abstract

“…In other words, when one vehicle deviates from its nominal trajectory in order to avert collision with an external obstacle, it must simultaneously consider the presence of cooperative vehicles sharing the same space. Some of the well known approaches that simultaneously address both of the requirements are based on potential fields, where agents are treated as charged particles and repel other cooperative or non-cooperative vehicles [17][18][19][20][21]. However, these techniques are often conservative, and they only provide guaranteed safety within small subsets of initial conditions and vehicle dynamics.…”

Section: Introductionmentioning

confidence: 99%

Time Coordination and Collision Avoidance Using Leader-Follower Strategies in Multi-Vehicle Missions

et al. 2021

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In recent years, the increasing popularity of multi-vehicle missions has been accompanied by a growing interest in the development of control strategies to ensure safety in these scenarios. In this work, we propose a control framework for coordination and collision avoidance in cooperative multi-vehicle missions based on a speed adjustment approach. The overall problem is decoupled in a coordination problem, in order to ensure coordination and inter-vehicle safety among the agents, and a collision-avoidance problem to guarantee the avoidance of non-cooperative moving obstacles. We model the network over which the cooperative vehicles communicate using tools from graph theory, and take communication losses and time delays into account. Finally, through a rigorous Lyapunov analysis, we provide performance bounds and demonstrate the efficacy of the algorithms with numerical and experimental results.

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“…In FANET, UAVs are mobile nodes. Micro and macro UAVs fly in the free air space with the speed of almost 30-60 km/h [5]. They have relatively large inter-node distances and a clear line of sight for direct communication.…”

Section: Introductionmentioning

confidence: 99%

Adaptive k-means clustering for Flying Ad-hoc Networks

Raza¹,

Khan²,

Maqsood³

et al. 2020

KSII TIIS

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Flying ad-hoc networks (FANETs) is a vibrant research area nowadays. This type of network ranges from various military and civilian applications. FANET is formed by micro and macro UAVs. Among many other problems, there are two main issues in FANET. Limited energy and high mobility of FANET nodes effect the flight time and routing directly. Clustering is a remedy to handle these types of problems. In this paper, an efficient clustering technique is proposed to handle routing and energy problems. Transmission range of FANET nodes is dynamically tuned accordingly as per their operational requirement. By optimizing the transmission range packet loss ratio (PLR) is minimized and link quality is improved which leads towards reduced energy consumption. To elect optimal cluster heads (CHs) based on their fitness we use k-means. Selection of optimal CHs reduce the routing overhead and improves energy consumption. Our proposed scheme outclasses the existing state-of-the-art techniques, ACO based CACONET and PSO based CLPSO, in terms of energy consumption and cluster building time.

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Avoidance maps: A new concept in UAV collision avoidance

Cited by 20 publications

References 17 publications

Energy and Delay Aware Physical Collision Avoidance in Unmanned Aerial Vehicles

Energy and Delay Aware Physical Collision Avoidance in Unmanned Aerial Vehicles

Time Coordination and Collision Avoidance Using Leader-Follower Strategies in Multi-Vehicle Missions

Adaptive k-means clustering for Flying Ad-hoc Networks

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