Adaptive behaviors in multi-agent source localization using passive sensing

Shaukat, Mansoor

doi:10.1177/1059712316664120

Cited by 13 publications

(7 citation statements)

References 55 publications

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“…In addition, their aim is more toward achieving single source localization rather than gradient following. Shaukat and Chitre (2016) presents an underwater application with autonomous robots to localize an RF beacon. Robots employ a method that eventually results in adaptive group coherence, depending on proximity to the source.…”

Section: Related Workmentioning

confidence: 99%

Collective gradient perception with a flying robot swarm

et al. 2022

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In this paper, we study the problem of collective and emergent sensing with a flying robot swarm in which social interactions among individuals lead to following the gradient of a scalar field in the environment without the need of any gradient sensing capability. We proposed two methods—desired distance modulation and speed modulation—with and without alignment control. In the former, individuals modulate their desired distance to their neighbors and in the latter, they modulate their speed depending on the social interactions with their neighbors and measurements from the environment. Methods are systematically tested using two metrics with different scalar field models, swarm sizes and swarm densities. Experiments are conducted using: (1) a kinematic simulator, (2) a physics-based simulator, and (3) real nano-drone swarm. Results show that using the proposed methods, a swarm—composed of individuals lacking gradient sensing ability—is able to follow the gradient in a scalar field successfully. Results show that when individuals modulate their desired distances, alignment control is not needed but it still increases the performance. However, when individuals modulate their speed, alignment control is needed for collective motion. Real nano-drone experiments reveal that the proposed methods are applicable in real-life scenarios.

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Section: Related Workmentioning

confidence: 99%

Collective gradient perception with a flying robot swarm

et al. 2022

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“…Several previous theoretical models of collective motion have found that adaptable interactions, i.e., interactions that can change according to the local environment, can improve the cohesiveness and performance of swarms [21][22][23] . Accordingly, in light of our experimental results, we sought to study the effect of the individual's ability to change its walking properties.…”

Section: Sociality-dependent Pwmentioning

confidence: 99%

Collective motion as a distinct behavioural state of the individual

Knebel

Sha-ked

Agmon

et al. 2020

Preprint

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AbstractCollective motion is an important biological phenomenon, ubiquitous among different organisms. Locusts are a quintessential example of collective motion: while displaying a minimal level of cooperation between individuals, swarms of millions are highly robust and persistent. Using desert locusts in a series of carefully controlled laboratory experiments, we show that locust coordinated marching induces a distinct behavioral mode in the individual that we term a “collective-motion-state”. Importantly, this state is not induced by aggregation or crowding per-se, but only following collective motion. It is manifested in specific walking behavior kinematics and has a lasting effect. Simulations confirm that the collective-motion-state provides an individual-based mechanism, which increases the robustness and stability of swarms in the presence of fluctuations. Overall, our findings suggest that collective-motion is more than an emergent property of the group, but a specific behavioral mode that is rooted in endogenous biological mechanisms of the individual.

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“…Recently, coordination control for multiagent systems (MASs) has received enormous attention due to its widespread applications, such as marine environment monitoring [1], multiships cooperative combat [2], and source localization [3,4]. Depending on the different theories, these coordination control problems are mainly classified into three types: consensus [5][6][7], flocking [8][9][10], and formation control [11,12].…”

Section: Introductionmentioning

confidence: 99%

Distributed Finite-Time Bipartite Consensus for Multiagent System with Event-Triggered Control

Xie

Liu

Yan

et al. 2020

Mathematical Problems in Engineering

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This paper investigates the distributed finite-time event-triggered bipartite consensus control for multiagent systems over antagonistic networks. Under the constraint of energy conservation, a distributed nonlinear finite-time control protocol only depending upon local information is proposed coupled with event-triggered strategies, where controllers of agents at triggered instants are only updated to reduce the computation. It is proved that when the antagonistic network is structurally balanced with a spanning tree, a necessary and sufficient condition is established to guarantee all agents to reach consensus values with identical magnitude but opposite signs. More interestingly, the settling time depending on the initial state is obtained over the whole process. Comparing to asymptotic control algorithms, the proposed control method has better disturbance rejection properties and convergence rate. Simulations are given to demonstrate the effectiveness of the theoretical results.

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Adaptive behaviors in multi-agent source localization using passive sensing

Cited by 13 publications

References 55 publications

Collective gradient perception with a flying robot swarm

Collective gradient perception with a flying robot swarm

Collective motion as a distinct behavioural state of the individual

Distributed Finite-Time Bipartite Consensus for Multiagent System with Event-Triggered Control

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