Decentralized information-theoretic task assignment for searching and tracking of moving targets

Moon, Sangwoo; Yang, Kwangjin; Gan, Seng Keat; Shim, David Hyunchul

doi:10.1109/icuas.2015.7152393

Cited by 10 publications

(9 citation statements)

References 19 publications

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“…Moon et al [68] proposed the use of probability density functions in coordination with a negotiation task assignment framework for UAS tasking. The algorithm uses information gathering-based task assignment with a two-layer framework.…”

Section: Moving Targetmentioning

confidence: 99%

Multiple-Scenario Unmanned Aerial System Control: A Systems Engineering Approach and Review of Existing Control Methods

2016

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Abstract:The use of unmanned aerial systems (UASs) in both the public and military environments is predicted to grow significantly. As the demand for UASs grows, the availability of more robust and capable vehicles that can perform multiple mission types will be needed. In the public sector, the demand will grow for UASs to be used for agriculture, forestry, and search and rescue missions. Militaries continue to demand more UAS capabilities for diverse operations around the world. Significant research has been performed and continues to progress in the areas of autonomous UAS control. A majority of the work focuses on subsets of UAS control: path planning, autonomy, small UAS controls, and sensors. Minimal work exists on a system-level problem of multiple-scenario UAS control for integrated systems. This paper provides a high-level modular system architecture definition that is modifiable across platform types and mission requirements. A review of the current research and employment of UAS capabilities is provided to evaluate the state of the capabilities required to enable the proposed architecture.

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Section: Moving Targetmentioning

confidence: 99%

Multiple-Scenario Unmanned Aerial System Control: A Systems Engineering Approach and Review of Existing Control Methods

2016

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“…The first area is revolving around the network structure of the UAVs, considering the network coverage utility [7] - [11], quality of the connectivity [12], [13] and the topology of the network [14]. The second area mainly focuses on deploying UAVs to cover targeted objects [15]. The third area focuses on using the UAVs as BSs to serve the UEs.…”

Section: Introductionmentioning

confidence: 99%

Intelligent UAV Deployment for a Disaster Resilient Wireless Network

Hydher¹,

Jayakody²,

Hemachandra³

et al. 2020

Preprint

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Deployment of unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) has been considered to be a feasible solution to provide network coverage in scenarios where the conventional terrestrial network is overloaded or inaccessible due to an emergency situation. This article studies the problem of optimal placement of the UAVs as ABSs to enable network connectivity to the users in a coverage free zone. The main contributions of this work include two approaches to position the UAVs and to assign user equipment (UE) to each UAV, such that the sum-rate and the coverage probability of the network is maximized. An approach can be selected depending on the prevailing scenario. The first approach uses clustering algorithm to determine the 2D positioning of the UAV and a matching algorithm is used for UE assignment by considering the characteristics of the air-to-ground propagation channels as well as the impact of co-channel interference from ABSs. Then it uses exhaustive search on different altitudes to find the optimal altitude. In the second approach, 2D positioning and UE assignment are done similarly to the first approach. However, the sub-optimal altitude is estimated using particle swarm optimization (PSO). The first approach is suitable for a system which has computational resource constraints or lower probability of line of sight (LoS) links. In contrast, the second approach is suitable for data rate greedy systems or a higher probability of LoS links.

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“…Networks of multiple agents have been widely applied to execute a variety of spatially distributed tasks in the recent literature. A multi-agent network can effectively expand the cover range and increase the efficiency compared with a single agent when the agents are applied for environmental exploration (Nestmeyer et al, 2017;Baranzadeh and Savkin, 2017;Li et al, 2017Li et al, , 2018, navigation (Liu et al, 2018;Geng et al, 2016;Fazlollahtabar, 2016), tracking (Moon et al, 2015;Newstadt et al, 2015;Cui et al, 2017) and target searching (Lanillos et al, 2014;Lanillos, 2013). In most applications, the connectivity maintenance of the network is a required precondition (Cortés, 2008;Olfati-Saber and Murray, 2004).…”

Section: Introductionmentioning

confidence: 99%

“…First, each agent moves to the area with maximum probability (Chung and Carpin, 2011;Lanillos et al, 2013). Second, each agent moves to the area with maximum uncertainty (Moon et al, 2015;Carpin et al, 2011). Bourgault et al (2003) used a single autonomous sensor platform to search for a single lost target.…”

Section: Introductionmentioning

confidence: 99%

Cooperative multi-agent search using Bayesian approach with connectivity maintenance

Xiao

Cui

2019

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Purpose This paper aims to present a distributed Bayesian approach with connectivity maintenance to manage a multi-agent network search for a target on a two-dimensional plane. Design/methodology/approach The Bayesian framework is used to compute the local probability density functions (PDFs) of the target and obtain the global PDF with the consensus algorithm. An inverse power iteration algorithm is introduced to estimate the algebraic connectivity λ2 of the network. Based on the estimated λ2, the authors design a potential field for the connectivity maintenance. Then, based on the detection probability function, the authors design a potential field for the search target. The authors combine the two potential fields and design a distributed gradient-based control for the agents. Findings The inverse power iteration algorithm can distributed estimate the algebraic connectivity by the agents. The agents can efficient search the target with connectivity maintenance with the designed distributed gradient-based search algorithm. Originality/value Previous study has paid little attention to the multi-agent search problem with connectivity maintenance. Our algorithm guarantees that the strongly connected graph of the multi-agent communication topology is always established while performing the distributed target search problem.

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Decentralized information-theoretic task assignment for searching and tracking of moving targets

Cited by 10 publications

References 19 publications

Multiple-Scenario Unmanned Aerial System Control: A Systems Engineering Approach and Review of Existing Control Methods

Multiple-Scenario Unmanned Aerial System Control: A Systems Engineering Approach and Review of Existing Control Methods

Intelligent UAV Deployment for a Disaster Resilient Wireless Network

Cooperative multi-agent search using Bayesian approach with connectivity maintenance

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