Collaborative Multi-MSA Multi-Target Tracking and Surveillance: a Divide &amp; Conquer Method Using Region Allocation Trees

Adamey, Emrah; Oğuz, Abdullah Ersan; Özgüner, Ü.

doi:10.1007/s10846-017-0499-4

Cited by 21 publications

(5 citation statements)

References 29 publications

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“…Consider a multi-agent system with one target agent followed by three tracking agents under the quasi-cyclic switched topologies, which is shown in Figure 2, where {φ 1 , 5), φ (6), φ(7)}, φ 3 = {φ(8), φ(9)}) and {φ 4 , φ 5 } ∈ N + us (φ 4 = {φ (10), φ (11), φ(12)}, φ 5 = {φ(13), φ (14), φ(15)}). (1)…”

Section: A Numerical Examplementioning

confidence: 99%

“…As an essential category of distributed artificial intelligence, MASs have been applied in the field of control engineering, such as the formation of spacecraft systems [5], robot systems [6,7], underwater vehicle systems [8], and unmanned aerial vehicles [9], etc. Tracking approaches for MASs have been widely used in areas of collaborative target tracking [10], convoy protection [11] and defensive navigation [12].…”

Section: Introductionmentioning

confidence: 99%

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Stability Analysis of Multi-Agent Tracking Systems with Quasi-Cyclic Switching Topologies

2020

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In this paper, the stability problem of a class of multi-agent tracking systems with quasi-cyclic switching topologies is investigated. The existing results of systems with switching topologies are usually achieved based on the assumption that the piecewise constant communication topologies are connected and the switchings are cyclic. The communication topologies are possible to be unconnected and it is difficult to guarantee the topologies switch circularly. The piecewise unconnected topology makes the interactive multi-agent tracking system to be an unstable subsystem over this time interval. In order to relax the assumption constraint, a quasi-cyclic method is proposed, which allows the topologies of multi-agent systems to switch in a less conservative way. Moreover, the stability of the tracking system with the existence of unstable subsystems is analyzed based on switched control theory. It is obtained that the convergence rate is affected by the maximum dwell time of unstable subsystems. Finally, a numerical example is provided to demonstrate the effectiveness of the theoretical results.

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Section: A Numerical Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stability Analysis of Multi-Agent Tracking Systems with Quasi-Cyclic Switching Topologies

2020

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“…ABSs frequently enable intelligent solving of a large diversity of reallife difficult problems, like, optimization of coordination of human groups online [44], and collaborative multisensor agents for multitarget tracking and surveillance [45]. Although several studies address intelligent problem-solving using ABSs, the evidence provided for their intelligence is usually intuitive, and there are several still open issues with respect to measures used for the assessment of their learning ability.…”

Section: State-of-the-art and Motivationmentioning

confidence: 99%

II-Learn—A Novel Metric for Measuring the Intelligence Increase and Evolution of Artificial Learning Systems

Iantovics¹,

Iakovidis²,

Nechita³

2019

IJCIS

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A novel accurate and robust metric called II-Learn for measuring the increase of intelligence of a system after a learning process is proposed. We define evolving learning systems, as systems that are able to make at least one measurable evolutionary step by learning. To prove the effectiveness of the metric we performed a case study, using a learning system. The universality of II-Learn is based on the fact that it does not depend on the architecture of the studied system.

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“…Another approach is to split the area into clusters, one for each vehicle, and solve a TSP for each cluster [11,12]. Other methods include negotiation-based algorithms [13], reinforcement learning [14] Markov decision process [15], divide-and-conquer algorithms [16],…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Cooperated Path Planning of Multiple Unmanned Aerial Vehicles Based on Revisit Time

Haghighi

Asadi

Delahaye

2021

Journal of Aerospace Information Systems

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This paper investigates multi-objective optimization of coordinated patrolling flight of multiple unmanned aerial vehicles in the vicinity of terrain, while respecting their performance parameters. A new efficient modified A-star (A*) algorithm with a novel defined criterion known as individual revisit time cell value is introduced and extended to the whole area of the 3D mountainous environment. As a contribution to solving trade-offs in the optimization problem, revisit time is conjugated with other contrary costs effective in flight planning through Pareto analysis. By introducing the revisit time and applying a specific setup to mitigate computational complexity, the proposed algorithm efficiently revisits the desired zones, which are more important to be revisited during the patrolling mission. The results of the introduced modified A* algorithm are compared in various scenarios with two different algorithms including a complete and optimal algorithm known as Dijkstra, and an evolutionary algorithm known as the genetic algorithm. Simulation results demonstrate that the proposed algorithm generates faster and more efficient trajectories in complex multiagent scenarios due to the introduced cell selection method and dynamic-based simplifications applied in this research.

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Collaborative Multi-MSA Multi-Target Tracking and Surveillance: a Divide & Conquer Method Using Region Allocation Trees

Cited by 21 publications

References 29 publications

Stability Analysis of Multi-Agent Tracking Systems with Quasi-Cyclic Switching Topologies

Stability Analysis of Multi-Agent Tracking Systems with Quasi-Cyclic Switching Topologies

II-Learn—A Novel Metric for Measuring the Intelligence Increase and Evolution of Artificial Learning Systems

Multi-Objective Cooperated Path Planning of Multiple Unmanned Aerial Vehicles Based on Revisit Time

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