Distributed task allocation and coordination scheme for a multi-UAV sensor network

Simi, S.; Kurup, R. Vimal; Rao, Sethuraman N

doi:10.1109/wocn.2013.6616189

Cited by 16 publications

(7 citation statements)

References 4 publications

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“…The objective of the approach is to find the position of the targets and minimize the total energy cost. Works related to task allocation and trajectory planning are also presented in [1,20,26].…”

Section: Related Workmentioning

confidence: 99%

Modelling the mobile target covering problem using flying drones

et al. 2015

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This paper addresses the mobile targets covering problem by using unmanned aerial vehicles (UAVs). It is assumed that each UAV has a limited initial energy and the energy consumption is related to the UAV's altitude. Indeed, the higher the altitude, the larger the monitored area and the higher the energy consumption. When an UAV runs out of battery, it is replaced by a new one. The aim is to locate UAVs in order to cover the piece of plane in which the target moves by using a minimum number of UAVs. Each target has to be monitored for each instant time.The problem under consideration is mathematically represented by defining mixed integer non-linear optimization models. Heuristic procedures are defined and they are based on restricted mixed integer programming (MIP) formulation of the problem. A computational study is carried out to assess the behaviour of the proposed models and MIP-based heuristics. A comparison in terms of efficiency and effectiveness among models and heuristics is carried out.

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Section: Related Workmentioning

confidence: 99%

Modelling the mobile target covering problem using flying drones

et al. 2015

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“…Multiple robots allow to achieve complex missions by assigning the single sub-tasks to different vehicles. Distributed task allocation for a multi-UAV sensor network is dealt with in [9], where each UAV distributes its remaining tasks dynamically to teammates based on its own resource availability. Multi-priority control has been exploited for floating-base manipulation in the last years: experimental results have been obtained for underwater systems in grasping operations [10] with a system characterized by 13 degrees of freedom taking into account several prioritized tasks run by means of proper activating functions.…”

Section: Introductionmentioning

confidence: 99%

Coordinated Control of Aerial Robotic Manipulators: Theory and Experiments

Muscio

Pierri

Trujillo

et al. 2018

IEEE Trans. Contr. Syst. Technol.

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This paper presents a three-layer control architecture for coordinated control of multiple aerial manipulators (UAVMs): the centralized top layer plans the end-effector desired trajectories of each UAVM; the middle layer, local to each vehicle, computes the corresponding motion references; the bottom layer is a low level dynamic motion controller, which tracks the motion references. At second layer, the overall mission is hierarchically decomposed in a set of elementary behaviors, which are combined together, via the Null Space-based Behavioral approach, into more complex compound behaviors. For each UAVM, the suitable compound behavior to be executed is selected by a supervisor. The proposed framework has been tested through an experimental campaign.

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“…In order to quickly and continuously chart a disaster site, operating several robotic systems in parallel is necessary. Coordination schemes for multiple UAS in joint operation have been investigated (Simi et al, 2013). There are attempts at controlling clouds of air contaminants by UAS networks (White et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Depending on the deployment scenario, the UAS will have to withstand electromagnetic interference (EMI) by strong RF sources. In previous studies, UAS operation in close proximity to radio broadcasting station has been investigated (Torrero et al, 2013). Other studies have been focussing on actually avoiding strong radar sources by flight path planning algorithms (Chen et al, 2014;Duan et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Definition and test of the electromagnetic immunity of UAS for first responders

et al. 2015

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Recent technological developments considerably lowered the barrier for unmanned aerial systems (UAS) to be employed in a variety of usage scenarios, comprising live video transmission from otherwise inaccessible vantage points. As an example, in the French-German ANCHORS project several UAS guided by swarm intelligence provide aerial views and environmental data of a disaster site while deploying an ad-hoc communication network for first responders. Since being able to operate in harsh environmental conditions is a key feature, the immunity of the UAS against radio frequency (RF) exposure has been studied. Conventional Electromagnetic Compatibility (EMC) applied to commercial and industrial electronics is not sufficient since UAS are airborne and can as such move beyond the bounds within which RF exposure is usually limited by regulatory measures. Therefore, the EMC requirements have been complemented by a set of specific RF test frequencies and parameters where strong sources are expected to interfere in the example project test case of an inland port environment. While no essential malfunctions could be observed up to field strengths of 30 V m−1, a sophisticated, more exhaustive approach for testing against potential sources of interference in key scenarios of UAS usage should be derived from our present findings.Recent technological developments considerably lowered the barrier for unmanned aerial systems (UAS) to be employed in a variety of usage scenarios, comprising live video transmission from otherwise inaccessible vantage points. As an example, in the French-German ANCHORS project several UAS guided by swarm intelligence provide aerial views and environmental data of a disaster site while deploying an ad-hoc communication network for first responders. Since being able to operate in harsh environmental conditions is a key feature, the immunity of the UAS against radio frequency (RF) exposure has been studied. Conventional Electromagnetic Compatibility (EMC) applied to commercial and industrial electronics is not sufficient since UAS are airborne and can as such move beyond the bounds within which RF exposure is usually limited by regulatory measures. Therefore, the EMC requirements have been complemented by a set of specific RF test frequencies and parameters where strong sources are expected to interfere in the example project test case of an inland port environment. While no essential malfunctions could be observed up to field strengths of 30 V m−1, a sophisticated, more exhaustive approach for testing against potential sources of interference in key scenarios of UAS usage should be derived from our present findings

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Distributed task allocation and coordination scheme for a multi-UAV sensor network

Cited by 16 publications

References 4 publications

Modelling the mobile target covering problem using flying drones

Modelling the mobile target covering problem using flying drones

Coordinated Control of Aerial Robotic Manipulators: Theory and Experiments

Definition and test of the electromagnetic immunity of UAS for first responders

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