<i>Temnothorax albipennis</i> migration inspired semi-flocking control for mobile sensor networks

Yuan, Wanmai; Ganganath, Nuwan; Cheng, Chi‐Tsun; Guo, Qing; Lau, Francis C. M.

doi:10.1063/1.5093073

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…From a control theory perspective, flocking algorithms have been combined with Kalman filters for efficient target tracking [20, 25-28, 31, 32]. The collective behavior of the ant species Temnothorax albipennis has inspired distributed coordination algorithms for tracking [33]. Neural networks can learn a priori unknown environments while dynamic coverage is achieved [29].…”

Section: 3mentioning

confidence: 99%

Efficient Evader Detection in Mobile Sensor Networks

Adams,

Ghosh,

Mask

et al. 2021

Preprint

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Suppose one wants to monitor a domain with sensors each sensing small ball-shaped regions, but the domain is hazardous enough that one cannot control the placement of the sensors. A prohibitively large number of randomly placed sensors would be required to obtain static coverage. Instead, one can use fewer sensors by providing only mobile coverage, a generalization of the static setup wherein every possible intruder is detected by the moving sensors in a bounded amount of time. Here, we use topology in order to implement algorithms certifying mobile coverage that use only local data to solve the global problem. Our algorithms do not require knowledge of the sensors' locations. We experimentally study the statistics of mobile coverage in two dynamical scenarios. We allow the sensors to move independently (billiard dynamics and Brownian motion), or to locally coordinate their dynamics (collective animal motion models). Our detailed simulations show, for example, that collective motion enhances performance: The expected time until the mobile sensor network achieves mobile coverage is lower for the D'Orsogna collective motion model than for the billiard motion model. Further, we show that even when the probability of static coverage is low, all possible evaders can nevertheless be detected relatively quickly by mobile sensors.

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Section: 3mentioning

confidence: 99%

Efficient Evader Detection in Mobile Sensor Networks

Adams,

Ghosh,

Mask

et al. 2021

Preprint

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“…Inspired by biological systems, flocking control focuses on the collective motion of multi-agent systems in which each agent has a limited communication distance, with applications ranging from swarm of unmanned aircraft vehicle (UAV) drones robots [14] to mobile sensor networks. [15] The core advantage of flocking control is that it guarantees collision avoidance, lattice-like formation, leader-tracking, connectivity preservation at the same time. However, most existing flocking algorithms, e.g., in Refs.…”

Section: Introductionmentioning

confidence: 99%

Memory-augmented adaptive flocking control for multi-agent systems subject to uncertain external disturbances

Wang¹,

Sun²,

Li³

et al. 2022

Chinese Phys. B

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This paper presents a novel flocking algorithm based on a memory-enhanced disturbance observer. To compensate for external disturbances, a filtered regressor for the double integrator model subject to external disturbances is designed to extract the disturbance information. With the filtered regressor method, the algorithm has the advantage of eliminating the need for acceleration information, thus reducing the sensor requirements in applications. Using the information obtained from the filtered regressor, a batch of stored data is used to design an adaptive disturbance observer, ensuring that the estimated values of the parameters of the disturbance system equation and the initial value converge to their actual values. The result is that the flocking algorithm can compensate for external disturbances and drive agents to achieve the desired collective behavior, including virtual leader tracking, inter-distance keeping, and collision avoidance. Numerical simulations verify the effectiveness of the algorithm proposed in the present study.

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“…Despite their constraints, many biological systems appear to implement algorithms that are robust, noise tolerant, yet efficient in time and communication. Some of the most intricate such algorithms are found in social insects, and they have inspired fascinating engineering designs such as route optimization (the traveling salesman problem) [1], task allocation among robot swarms [2], and mobile sensor networks [3].…”

Section: Introductionmentioning

confidence: 99%

The Power of Social Information in Ant-Colony House-Hunting: A Computational Modeling Approach

Lynch

Pratt

2020

Preprint

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The decentralized cognition of animal groups is both a challenging biological problem and a potential basis for bio-inspired design. The understanding of these systems and their application can benefit from modeling and analysis of the underlying algorithms. In this study, we define a modeling framework that can be used to formally represent all components of such algorithms. As an example application of the framework, we adapt to it the much-studied house-hunting algorithm used by emigrating colonies of Temnothorax ants to reach consensus on a new nest. We provide a Python simulator that encodes accurate individual behavior rules and produces simulated behaviors consistent with empirical observations, on both the individual and group levels. Our model successfully reproduces experimental results showing the high cognitive capacity of colonies, their rational time investment during decision-making, and their ability to avoid and repair splits with the help of social information. We also use the model to make predictions about several unstudied aspects of emigration behavior. The results suggest the value of individual sensitivity to site population for ensuring consensus, and they indicate a more complex relationship between individual behavior and the speed/accuracy trade-off than previously appreciated. The model proved relatively weak at resolving colony divisions among multiple sites, suggesting either limits to the ants’ ability to reach consensus, or an aspect of their behavior not captured in our model. It is our hope that these insights and predictions can inspire further research from both the biology and computer science community.

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Temnothorax albipennis migration inspired semi-flocking control for mobile sensor networks

Cited by 5 publications

References 22 publications

Efficient Evader Detection in Mobile Sensor Networks

Efficient Evader Detection in Mobile Sensor Networks

Memory-augmented adaptive flocking control for multi-agent systems subject to uncertain external disturbances

The Power of Social Information in Ant-Colony House-Hunting: A Computational Modeling Approach

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