Explicit vs. Tacit leadership in influencing the behavior of swarms

Amraii, Saman Amirpour; Walker, Phillip; Lewis, Michael; Chakraborty, Nilanjan; Sycara, Katia

doi:10.1109/icra.2014.6907164

Cited by 11 publications

(4 citation statements)

References 16 publications

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“…To assess connectivity this way, the Fiedler value can be used, as seen in refs. [13, 18, 19]. It refers to the second smallest eigenvalue of the Laplacian matrix , where is graph degree matrix and the adjacency one.…”

Section: Related Workmentioning

confidence: 99%

Adversarial scenarios for herding UAVs and counter-swarm techniques

Vasquez

Barca²

2023

Robotica

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The present paper aims to design and simulate an adversarial strategy where a swarm of quadrotor UAVs is herding anti-aircraft land vehicles (AALV) that actively oppose the swarm’s objective by potentially taking them down. The main strategy is to block the AALVs’ line of sight to their goal zone (AALVs’ objective), shifting its trajectory so it reaches a kill zone instead (UAVs’ objective). The counter-swarm strategy performed by the AALVs consists of taking down the closest aerial units to the goal zone. As a result, a consensus algorithm is executed by the UAVs in order to assess the communication network and re-group. Consensus is based on the propagation of local observations that converge into a global agreement on a communication graph. Re-grouping is done via positioning around the kill zone vector or preferring an anti-clockwise formation to better close gaps. The adversarial strategy was tested in an empty arena and urban setting, the latter making use of a path-planning procedure that re-routes the AALV trajectory based on its current destination. Simulation results show a maximum UAV mission success rate converging to roughly 80% in the empty arena. When targeted elimination procedures are executed, UAV mission performance drops 5%, making no distinction between re-grouping strategies in the empty arena. The urban setting shows lower performance due to navigation complexity but favors the decision to re-group based on a formation that close gaps rather than positioning around the kill zone vector.

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Section: Related Workmentioning

confidence: 99%

Adversarial scenarios for herding UAVs and counter-swarm techniques

Vasquez

Barca²

2023

Robotica

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“…A well-known example of this scheme is flocking, which is typically deployed in larger robot teams [22]. The movement of the team can be directed by a tacit leader [23], which is a robot that does not explicitly identify itself as a leader to other robots. Instead, it follows a reference trajectory itself, and thus, due to flock cohesion, causes the group to move with it in the desired direction.…”

Section: A Backgroundmentioning

confidence: 99%

An Adversarial Approach to Private Flocking in Mobile Robot Teams

Zheng

Panerati

Beltrame

et al. 2020

IEEE Robot. Autom. Lett.

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Privacy is an important facet of defence against adversaries. In this letter, we introduce the problem of private flocking. We consider a team of mobile robots flocking in the presence of an adversary, who is able to observe all robots' trajectories, and who is interested in identifying the leader. We present a method that generates private flocking controllers that hide the identity of the leader robot. Our approach towards privacy leverages a data-driven adversarial co-optimization scheme. We design a mechanism that optimizes flocking control parameters, such that leader inference is hindered. As the flocking performance improves, we successively train an adversarial discriminator that tries to infer the identity of the leader robot. To evaluate the performance of our co-optimization scheme, we investigate different classes of reference trajectories. Although it is reasonable to assume that there is an inherent trade-off between flocking performance and privacy, our results demonstrate that we are able to achieve high flocking performance and simultaneously reduce the risk of revealing the leader.

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“…The authors found that when humans control a swarm of robots, certain types of topologies increased the workload. Walker et al (2013) and Amraii et al (2014) investigated the effect of two command propagation methods (i.e., methods to propagate a command issued by a human being to all the robots of a swarm) when a human operator guides a leader robot (i.e., a single robot). In their work, a human operator guides the leader robot by changing the leader robot's velocity and heading through a graphical interface.…”

Section: Related Literaturementioning

confidence: 99%

Investigating the effect of the reality gap on the human psychophysiological state in the context of human-swarm interaction

Podevijn

O’Grady

Fantini‐Hauwel

et al. 2016

PeerJ Computer Science

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The reality gap is the discrepancy between simulation and reality-the same behavioural algorithm results in different robot swarm behaviours in simulation and in reality (with real robots). In this paper, we study the effect of the reality gap on the psychophysiological reactions of humans interacting with a robot swarm. We compare the psychophysiological reactions of 28 participants interacting with a simulated robot swarm and with a real (non-simulated) robot swarm. Our results show that a real robot swarm provokes stronger reactions in our participants than a simulated robot swarm. We also investigate how to mitigate the effect of the reality gap (i.e., how to diminish the difference in the psychophysiological reactions between reality and simulation) by comparing psychophysiological reactions in simulation displayed on a computer screen and psychophysiological reactions in simulation displayed in virtual reality. Our results show that our participants tend to have stronger psychophysiological reactions in simulation displayed in virtual reality (suggesting a potential way of diminishing the effect of the reality gap).

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Explicit vs. Tacit leadership in influencing the behavior of swarms

Cited by 11 publications

References 16 publications

Adversarial scenarios for herding UAVs and counter-swarm techniques

Adversarial scenarios for herding UAVs and counter-swarm techniques

An Adversarial Approach to Private Flocking in Mobile Robot Teams

Investigating the effect of the reality gap on the human psychophysiological state in the context of human-swarm interaction

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