Single Agent Herding of n-Agents: A Switched Systems Approach

Licitra, Ryan A.; Hutcheson, Zachary D.; Doucette, Emily A.; Dixon, Warren E.

doi:10.1016/j.ifacol.2017.08.2020

Cited by 28 publications

(20 citation statements)

References 19 publications

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“…In Licitra et al (2017Licitra et al ( , 2018, the authors discuss herding using a switched-system approach; the herder (defender) chases targets (evaders/attackers) sequentially by switching among them so that certain dwell-time conditions are satisfied to guarantee stability of the resulting trajectories. However, the assumption that only one of the targets is influenced by the herder at any time might be limiting and non-practical in real applications.…”

Section: Related Workmentioning

confidence: 99%

“…Most of the aforementioned approaches for herding are limiting due to one or some of the following aspects: (1) simplified motion models (Varava et al, 2017;Pierson and Schwager, 2018), (2) absence of obstacles in the environment (Licitra et al, 2017(Licitra et al, , 2018Paranjape et al, 2018), (3) no consideration of intra-team collisions (Varava et al, 2017;Pierson and Schwager, 2018), ( 4) assumption on a particular form of potential field to model the repulsive motion of the attackers with respect to the defenders (Licitra et al, 2017(Licitra et al, , 2018Paranjape et al, 2018;Pierson and Schwager, 2018).…”

Section: Related Workmentioning

confidence: 99%

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Aerial Swarm Defense by StringNet Herding: Theory and Experiments

2021

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This paper studies a defense approach against one or more swarms of adversarial agents. In our earlier work, we employed a closed formation (“StringNet”) of defending agents (defenders) around a swarm of adversarial agents (attackers) to confine their motion within given bounds, and guide them to a safe area. The adversarial agents were assumed to remain close enough to each other, i.e., within a prescribed connectivity region. To handle situations when the attackers no longer stay within such a connectivity region, but rather split into smaller swarms (clusters) to maximize the chance or impact of attack, this paper proposes an approach to learn the attacking sub-swarms and reassign defenders toward the attackers. We use a “Density-based Spatial Clustering of Application with Noise (DBSCAN)” algorithm to identify the spatially distributed swarms of the attackers. Then, the defenders are assigned to each identified swarm of attackers by solving a constrained generalized assignment problem. We also provide conditions under which defenders can successfully herd all the attackers. The efficacy of the approach is demonstrated via computer simulations, as well as hardware experiments with a fleet of quadrotors.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Aerial Swarm Defense by StringNet Herding: Theory and Experiments

2021

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“…which deals with the running costs of the circumvention controls. Note that if the final error can be reduced to the order of δ 1 , then the simulation with (14) produces optimal strategies which mainly minimizes κ c (·) L 2 (0,t f ) . Pursuit control Circumvention control Figure 6.…”

Section: 21mentioning

confidence: 99%

“…Pursuit control Circumvention control Figure 6. Diagrams for the optimal control leading to u e (t f ) ≃ (−1, 1) with the cost (14). The positions at the integer times and the final time are marked by circles.…”

Section: 21mentioning

confidence: 99%

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Asymptotic behavior and control of a “guidance by repulsion” model

Zuazua

2020

Math. Models Methods Appl. Sci.

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We model and analyze a herding problem, where the drivers try to steer the evaders' trajectories while the evaders always move away from the drivers. This problem is motivated by the guidance-by-repulsion model [10], where the authors answer how to control the evaders' positions and what is the optimal maneuver of the drivers. First, we obtain the well-posedness and the long-time behavior of the one-driver and one-evader model, assuming of the same friction coefficients. In this case, the exact controllability is proved in a long enough time horizon. We extend the model to the multi-driver and multi-evader case, and develop numerical simulations to systematically explore the nature of controlled dynamics in various scenarios. The optimal strategies turn out to share a common pattern to the one-driver and one-evader case: the drivers rapidly occupy the position behind the target, and want to pursuit evaders in a straight line for most of the time. Inspired by this, we build a feedback strategy which stabilizes the direction of evaders.Date: November 5, 2019.

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