Multi-UAV Surveillance With Minimum Information Idleness and Latency Constraints

Scherer, Jürgen; Rinner, Bernhard

doi:10.1109/lra.2020.3003884

Cited by 45 publications

(13 citation statements)

References 26 publications

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“…Drone swarms have further extended advantages as they can cover much larger areas in shorter times. With the ability of having on-board intelligence, UAVs not only are able to collect intelligence information about automatically detected and identified objects, but are also able, as a fleet, to self-coordinate their tasks and collaborate in order to accomplish surveillance tasks such as optimal area coverage given vehicle and sensors constraints [14,15], convoy protection to a group of ground vehicles [16], and persistent surveillance [17,18].…”

Section: Security and Surveillancementioning

confidence: 99%

Aerial Swarms: Recent Applications and Challenges

et al. 2021

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Purpose of Review Currently, there is a large body of research on multi-agent systems addressing their different system theoretic aspects. Aerial swarms as one type of multi-agent robotic systems have recently gained huge interest due to their potential applications. However, aerial robot groups are complex multi-disciplinary systems and usually research works focus on specific system aspects for particular applications. The purpose of this review is to provide an overview of the main motivating applications that drive the majority of research works in this field, and summarize fundamental and common algorithmic components required for their development. Recent FindingsMost system demonstrations of current aerial swarms are based on simulations, some have shown experiments using few 10 s of robots in controlled indoor environment, and limited number of works have reported outdoor experiments with small number of autonomous aerial vehicles. This indicates scalability issues of current swarm systems in real world environments. This is mainly due to the limited confidence on the individual robot's localization, swarm-level relative localization, and the rate of exchanged information between the robots that is required for planning safe coordinated motions.Summary This paper summarizes the main motivating aerial swarm applications and the associated research works. In addition, the main research findings of the core elements of any aerial swarm system, state estimation and mission planning, are also presented. Finally, this paper presents a proposed abstraction of an aerial swarm system architecture that can help developers understand the main required modules of such systems.

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Section: Security and Surveillancementioning

confidence: 99%

Aerial Swarms: Recent Applications and Challenges

et al. 2021

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“…The set of targets K l can be determined based on the users served in the lth batch and the relationship between the user's demand for targets. UAVs need to plan paths that can traverse all the targets in K l during the lth batch's reconnaissance phase, which is similar to the multiple traveling salesman problem [26]. To solve this problem, people usually use graph theory to construct targets as an undirected graph and find multiple routes that can traverse all the vertices in the graph with the least total cost [27].…”

Section: Planning Path In the Reconnaissancementioning

confidence: 99%

A User-Priority-Driven Multi-UAV Cooperative Reconnaissance Strategy

Liu

Zhao

et al. 2021

International Journal of Aerospace Engineering

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This paper considers a reconnaissance scenario where multiple unmanned aerial vehicles (UAVs) provide services cooperatively for multiple users with different priorities. Although users all expect to acquire their needed information in a short time, the degree of urgency to meet their demands is different when taking their diverse priorities into account. A priority-driven multiuser satisfaction model is designed, where users’ satisfaction is quantified based on their different priorities, information acquisition demands, and the time they obtain their desired information. To ensure high priority users’ fast information acquisition while avoiding excessive delay in low priority users’ information acquisition, a batchwise information backhaul strategy is adopted. In each batch, a UAV is selected as the data ferry through a consultative mechanism to carry information back to users. This reconnaissance process is formulated as a cooperative path planning problem, where the optimization objective is maximizing users’ total satisfaction, while an intelligent algorithm is proposed to solve this problem effectively. The simulation results show that compared with traditional path planning algorithms without considering user satisfaction, our proposed algorithm can guarantee faster information acquisition for users with higher priorities, which leads to higher satisfaction. In addition, the applicability of our proposed reconnaissance strategy and path planning algorithm in different situations is also analyzed.

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“…Unmanned Aerial Vehicles (UAVs), due to their great versatility, have been recently used for tasks such as smart farming [ 1 ], surveillance [ 2 , 3 , 4 ], wildfire control [ 5 , 6 ], delivery [ 7 , 8 , 9 ], and natural disaster situations [ 10 , 11 ] among others. UAVs are mainly classified into two categories: fixed-wing and rotary wing.…”

Section: Introductionmentioning

confidence: 99%

Multi UAV Coverage Path Planning in Urban Environments

Muñoz

López

Quevedo

et al. 2021

Sensors

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Coverage path planning (CPP) is a field of study which objective is to find a path that covers every point of a certain area of interest. Recently, the use of Unmanned Aerial Vehicles (UAVs) has become more proficient in various applications such as surveillance, terrain coverage, mapping, natural disaster tracking, transport, and others. The aim of this paper is to design efficient coverage path planning collision-avoidance capable algorithms for single or multi UAV systems in cluttered urban environments. Two algorithms are developed and explored: one of them plans paths to cover a target zone delimited by a given perimeter with predefined coverage height and bandwidth, using a boustrophedon flight pattern, while the other proposed algorithm follows a set of predefined viewpoints, calculating a smooth path that ensures that the UAVs pass over the objectives. Both algorithms have been developed for a scalable number of UAVs, which fly in a triangular deformable leader-follower formation with the leader at its front. In the case of an even number of UAVs, there is no leader at the front of the formation and a virtual leader is used to plan the paths of the followers. The presented algorithms also have collision avoidance capabilities, powered by the Fast Marching Square algorithm. These algorithms are tested in various simulated urban and cluttered environments, and they prove capable of providing safe and smooth paths for the UAV formation in urban environments.

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Multi-UAV Surveillance With Minimum Information Idleness and Latency Constraints

Cited by 45 publications

References 26 publications

Aerial Swarms: Recent Applications and Challenges

Aerial Swarms: Recent Applications and Challenges

A User-Priority-Driven Multi-UAV Cooperative Reconnaissance Strategy

Multi UAV Coverage Path Planning in Urban Environments

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