Multiple UAV Systems: A Survey

Skorobogatov, Georgy; Barrado, Cristina; Salamí, Esther

doi:10.1142/s2301385020500090

Cited by 166 publications

(79 citation statements)

References 102 publications

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“…The track separation is calculated from the width of the observed area. And the width of the observed area can be calculated from equation (1) of "Multiple UAV cooperative searching operation using polygon area decomposition and efficient coverage algorithms" 4…”

Section: Trajectories Assignmentmentioning

confidence: 99%

“…One possibility to overcome this limitation is to use a system with several UAVs. 1 A system with multiple UAVs is useful for collecting information in parallel over vast areas, or when there are time constraints, a single unmanned aerial system cannot complete the task. Systems with multiple UAVs are also fault-tolerant and flexible; if one UAV fails, it will not hinder the mission from being completed.…”

Section: Introductionmentioning

confidence: 99%

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Flight planning in multi-unmanned aerial vehicle systems: Nonconvex polygon area decomposition and trajectory assignment

Skorobogatov

Barrado

Salamí

et al. 2021

International Journal of Advanced Robotic Systems

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Nowadays, it is quite common to have one unmanned aerial vehicle (UAV) working on a task but having a team of UAVs is still rare. One of the problems that prevent us from using teams of UAVs more frequently is flight planning. In this work, we present the first open-source solution ( https://pypi.org/project/pode/ ) for splitting any complex area into multiple parts. The area of interest can be convex or nonconvex and can include any number of no-flight zones. Four solutions, based on the algorithm of Hert and Lumelsky, are tested with the aim of improving the compactness of the partitions. We also show how the shape of the partitions influences flight performance in a real case scenario.

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Section: Trajectories Assignmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flight planning in multi-unmanned aerial vehicle systems: Nonconvex polygon area decomposition and trajectory assignment

Skorobogatov

Barrado

Salamí

et al. 2021

International Journal of Advanced Robotic Systems

Self Cite

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Section: Human-uav Cooperationmentioning

confidence: 99%

“…The balance between operators and UAVs was categorized in [39] and [40] which examine various control architectures. In [39] the authors describe different architectures that have been seen in UAV-human systems such as how and when operators interact with UAVs, how UAVs determine trajectories, and how much autonomy UAVs are given. The author in [40] likewise compares implementations of human-robot systems and suggests that an ideal architecture would adjust its complexity depending on the level of control needed.…”

Section: Human-uav Cooperationmentioning

confidence: 99%

Gesture commands for controlling high-level UAV behavior

Akagi¹,

Morris²,

Moon³

et al. 2021

SN Appl. Sci.

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Directing groups of unmanned air vehicles (UAVs) is a task that typically requires the full attention of several operators. This can be prohibitive in situations where an operator must pay attention to their surroundings. In this paper we present a gesture device that assists operators in commanding UAVs in focus-constrained environments. The operator influences the UAVs’ behavior by using intuitive hand gesture movements. Gestures are captured using an accelerometer and gyroscope and then classified using a logistic regression model. Ten gestures were chosen to provide behaviors for a group of fixed-wing UAVs. These behaviors specified various searching, following, and tracking patterns that could be used in a dynamic environment. A novel variant of the Monte Carlo Tree Search algorithm was developed to autonomously plan the paths of the cooperating UAVs. These autonomy algorithms were executed when their corresponding gesture was recognized by the gesture device. The gesture device was trained to classify the ten gestures and accurately identified them 95% of the time. Each of the behaviors associated with the gestures was tested in hardware-in-the-loop simulations and the ability to dynamically switch between them was demonstrated. The results show that the system can be used as a natural interface to assist an operator in directing a fleet of UAVs. Article highlights A gesture device was created that enables operators to command a group of UAVs in focus-constrained environments. Each gesture triggers high-level commands that direct a UAV group to execute complex behaviors. Software simulations and hardware-in-the-loop testing shows the device is effective in directing UAV groups.

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“…Because tasks faced by UAVs are increasingly complex, a single UAV is usually not capable of performing extensive tasks, such as express transportation, disaster relief, surveying and mapping, and power-line inspection. With in-depth research on artificial intelligence technology, UAVs have tended to gradually become more autonomous and intelligent, and a significant shift of focus occurred as researchers began to investigate problems involving UAV groups rather than a single UAV (Mohiuddin et al, 2020;Skorobogatov et al, 2020). Coordinating the performance of UAV tasks can dramatically improve the effectiveness of entire systems from the viewpoint of robustness, reliability, and performance.…”

Section: Introductionmentioning

confidence: 99%

A review of cooperative path planning of an unmanned aerial vehicle group

Zhang

Xin

Dou

et al. 2020

Front Inform Technol Electron Eng

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As a cutting-edge branch of unmanned aerial vehicle (UAV) technology, the cooperation of a group of UAVs has attracted increasing attention from both civil and military sectors, due to its remarkable merits in functionality and flexibility for accomplishing complex extensive tasks, e.g., search and rescue, fire-fighting, reconnaissance, and surveillance. Cooperative path planning (CPP) is a key problem for a UAV group in executing tasks collectively. In this paper, an attempt is made to perform a comprehensive review of the research on CPP for UAV groups. First, a generalized optimization framework of CPP problems is proposed from the viewpoint of three key elements, i.e., task, UAV group, and environment, as a basis for a comprehensive classification of different types of CPP problems. By following the proposed framework, a taxonomy for the classification of existing CPP problems is proposed to describe different kinds of CPPs in a unified way. Then, a review and a statistical analysis are presented based on the taxonomy, emphasizing the coordinative elements in the existing CPP research. In addition, a collection of challenging CPP problems are provided to highlight future research directions.

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Multiple UAV Systems: A Survey

Cited by 166 publications

References 102 publications

Flight planning in multi-unmanned aerial vehicle systems: Nonconvex polygon area decomposition and trajectory assignment

Flight planning in multi-unmanned aerial vehicle systems: Nonconvex polygon area decomposition and trajectory assignment

Gesture commands for controlling high-level UAV behavior

A review of cooperative path planning of an unmanned aerial vehicle group

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