Systemic design of distributed multi-UAV cooperative decision-making for multi-target tracking

Zhao, Yu-xin; Wang, Xiangke; Wang, Chang; Cong, Yirui; Shen, Lincheng

doi:10.1007/s10458-019-09401-5

Cited by 49 publications

(14 citation statements)

References 46 publications

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“…Indeed, examples of these applications are those related to multirobot systems for the observation of multiple moving targets, for which different control approaches already exist, such as cooperative multi-robot observation of multiple moving targets (CMOMMT), cooperative search-acquisition-track (CSAT), multi-robot pursuit evasion (MPE) [17]. In addition, multi-drone systems have been analyzed in terms of distributed multi-agent systems for multi-target tracking problems [18]. In the end, the environmental domain presents some advanced multi-drone solutions for environmental monitoring of dynamic natural threats, such as the ones for tracking the dispersion of contaminant clouds [19].…”

Section: Cooperative Drone-based Counter-uas Systemsmentioning

confidence: 99%

A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones

Castrillo

Manco

Pascarella

et al. 2022

Drones

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In recent years, the drone market has had a significant expansion, with applications in various fields (surveillance, rescue operations, intelligent logistics, environmental monitoring, precision agriculture, inspection and measuring in the construction industry). Given their increasing use, the issues related to safety, security and privacy must be taken into consideration. Accordingly, the development of new concepts for countermeasures systems, able to identify and neutralize a single (or multiples) malicious drone(s) (i.e., classified as a threat), has become of primary importance. For this purpose, the paper evaluates the concept of a multiplatform counter-UAS system (CUS), based mainly on a team of mini drones acting as a cooperative defensive system. In order to provide the basis for implementing such a system, we present a review of the available technologies for sensing, mitigation and command and control systems that generally comprise a CUS, focusing on their applicability and suitability in the case of mini drones.

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Section: Cooperative Drone-based Counter-uas Systemsmentioning

confidence: 99%

A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones

Castrillo

Manco

Pascarella

et al. 2022

Drones

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“…A formulation based on the unscented Kalman filter is used to optimally estimate and predict the location of the ground targets, while the allocation of targets is continuously updated among the cooperating agents using consensus decision-making. Zhao et al [ 14 ] also addressed the cooperative decision-making problem for multi-target tracking when using a team of UAVs. In this case, the tracking decisions are made under the framework of distributed multi-agent partially-observable Markov decision processes.…”

Section: State-of-the-art and The Main Contributionsmentioning

confidence: 99%

“…The use of multiple aerial vehicles for detection, surveillance, and tracking of multiple targets has largely been covered in the literature [ 11 , 12 ]. Fewer studies have been devoted to the second category, where a shared-attention strategy for a single aerial vehicle or a coordinated team is addressed [ 13 , 14 , 15 ]. This second category is nevertheless quite appealing, aiming to accomplish the task with minimum resource deployment while making the most out of the available resources.…”

Section: Introductionmentioning

confidence: 99%

Flying Chameleons: A New Concept for Minimum-Deployment, Multiple-Target Tracking Drones

Vargas

Vivas

Rubio

et al. 2022

Sensors

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In this paper, we aim to open up new perspectives in the field of autonomous aerial surveillance and target tracking systems, by exploring an alternative that, surprisingly, and to the best of the authors’ knowledge, has not been addressed in that context by the research community thus far. It can be summarized by the following two questions. Under the scope of such applications, what are the implications and possibilities offered by mounting several steerable cameras onboard of each aerial agent? Second, how can optimization algorithms benefit from this new framework, in their attempt to provide more efficient and cost-effective solutions on these areas? The paper presents the idea as an additional degree of freedom to be exploited, which can enable more efficient alternatives in the deployment of such applications. As an initial approach, the problem of the optimal positioning with respect to a set of targets of one single agent, equipped with several onboard tracking cameras with different or variable focal lengths, is addressed. As a consequence of this allowed heterogeneity in focal lengths, the notion of distance needs to be adapted into a notion of optical range, as the agent can trade longer Euclidean distances for correspondingly longer focal lengths. Moreover, the proposed optimization indices try to balance, in an optimal way, the verticality of the viewpoints along with the optical range to the targets. Under these premises, several positioning strategies are proposed and comparatively evaluated.

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“…More specifically, the first block of this layer (i.e., individual cognition 1) is responsible for a more advanced tactical perception by processing and analyzing the processed image recognition [ 30 ] and 3D reconstruction algorithms. The second block (i.e., individual cognition 2) is responsible for strategical reasoning, and it consumes the information passed by former layers and executes decision-making algorithms [ 41 ], e.g., rule-based expert systems [ 42 ]. The decision-making in this level has a database of scenarios in which the system takes advantage to make analogous actions for similar cases.…”

Section: The Aerial Robotics Cognitive Architecturementioning

confidence: 99%

A Robotic Cognitive Architecture for Slope and Dam Inspections

Pinto

Honório

Melo

et al. 2020

Sensors

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Big construction enterprises, such as electrical power generation dams and mining slopes, demand continuous visual inspections. The sizes of these structures and the necessary level of detail in each mission requires a conflicting set of multi-objective goals, such as performance, quality, and safety. It is challenging for human operators, or simple autonomous path-following drones, to process all this information, and thus, it is common that a mission must be repeated several times until it succeeds. This paper deals with this problem by developing a new cognitive architecture based on a collaborative environment between the unmanned aerial vehicles (UAVs) and other agents focusing on optimizing the data gathering, information processing, and decision-making. The proposed architecture breaks the problem into independent units ranging from sensors and actuators up to high-level intelligence processes. It organizes the structures into data and information; each agent may request an individual behavior from the system. To deal with conflicting behaviors, a supervisory agent analyzes all requests and defines the final planning. This architecture enables real-time decision-making with intelligent social behavior among the agents. Thus, it is possible to process and make decisions about the best way to accomplish the mission. To present the methodology, slope inspection scenarios are shown.

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Systemic design of distributed multi-UAV cooperative decision-making for multi-target tracking

Cited by 49 publications

References 46 publications

A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones

A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones

Flying Chameleons: A New Concept for Minimum-Deployment, Multiple-Target Tracking Drones

A Robotic Cognitive Architecture for Slope and Dam Inspections

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