A Self-organizing Cooperative Hunting by Swarm Robotic Systems Based on Loose-preference Rule

Huang, Tian-Yun; Chen, Xuebo; Xu, Wei; Zhou, Ziwei; Ren, Zhi-Yong

doi:10.1016/s1874-1029(13)60007-5

Cited by 18 publications

(6 citation statements)

References 11 publications

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“…Andreas Kolling et al [37] present the human swarm interaction survey. Huang et al [38] presented the cooperative hunting behavior mathematically. On the basis of decomposition of hunting behavior, the loose preference rule is considered as an interaction among individuals and the targets for ideal hunting.…”

Section: Multi-robots Hunting Tasks and Approachesmentioning

confidence: 99%

A novel bio- inspired algorithm for hunting in multi robot scenario

Agrawal

Potdar

2019

IJEECS

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In a multi-robot scenario, cooperative hunting is a key issue when a group of robots are hunting for evader/evaders and when the location of the evader is continuously changing. Cooperative hunting is addressed in this paper by proposing a novel bio inspired Corner Dragging Algorithm (CDA). Corner Dragging Algorithm operates by making an alliance of robots that drag the evader towards any one of the four corners; whichever is closest to the evader. Different shapes of obstacles are avoided during this pursuit. While developing the Corner Dragging Algorithm, we analyze the shortcomings and advantages of some of the existing algorithms including dynamic alliance and formation construction algorithm and incorporate these changes in our design to achieve improved results. Performance of the algorithm is evaluated on the basis of simulation in MATLAB.

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Section: Multi-robots Hunting Tasks and Approachesmentioning

confidence: 99%

A novel bio- inspired algorithm for hunting in multi robot scenario

Agrawal

Potdar

2019

IJEECS

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“…By optimizing the cost function describing the surrounding effect, Li et al 27 dynamically set the optimal hunting point by using the negotiation method according to the target position, which realizes online hunting. Based on a loose preference rule established, Huang et al 28 form an ideal hunting formation through the interaction between the target and the hunters. With a gray wolf tracking strategy applied, Xie et al 29 realize the adaptive formation tracking encirclement control based on neighboring relative state information.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning method for target hunting control of multi‐robot systems with obstacles

Fan

Yang

Liu

et al. 2022

Int J of Intelligent Sys

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Aiming at the target encirclement problem of multi‐robot systems, a target hunting control method based on reinforcement learning is proposed. First, the Markov game modeling for the multi‐robot system is carried out. According to the task of hunting, potential energy models are designed to meet the requirements of arriving at the desired state and avoiding obstacles. The multi‐robot reinforcement learning algorithm guided by the potential energy models is presented to perform the hunting, where reinforcement learning principles are combined with the model control. Secondly, based on the potential energy models, the target‐tracking hunting strategy and the target‐circumnavigation hunting strategy are established. In the former, the consensus tracking of multi‐robot systems is achieved by designing the velocity potential energy function. And in the latter, virtual circumnavigation points are added to construct the potential energy function, which realizes the desired circumnavigation. Finally, the effectiveness of target hunting control based on the multi‐robot reinforcement learning method is verified by simulation.

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“…Studying this phenomena provides an insight into natural interactions, such as prey escape strategies (Breakwell, 1975;Bhattacharya et al, 2011Bhattacharya et al, , 2014Yang et al, 2014;Zha et al, 2016;Zhang et al, 2019), collective behavior (Neill and Cullen, 1974;Siegfried and Underhill, 1975;Bertram, 1978), catching efficiency for predators (Iwama and Sato, 2012;Saito et al, 2016;Masuko et al, 2017;Janosov et al, 2017) and the optimal number of predators for predation success (Kamimura and Ohira, 2010;Vicsek, 2010). But this approach can also provide elegant solutions for artificial systems, including the design of target trapping by autonomous robots (Antonelli et al, 2007;Huang et al, 2013;Peng et al, 2016;.…”

Section: Introductionmentioning

confidence: 99%

Synchronous intercept strategies for a robotic defense-intrusion game with two defenders

et al. 2020

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We study the defense-intrusion game, in which a single attacker robot tries to reach a stationary target that is protected by two defender robots. We focus on the "synchronous intercept problem", where both robots have to reach the attacker robot synchronously to intercept it. Assume that the attacker robot has the control policy which is based on attraction to the target and repulsion from the defenders, two kinds of synchronous intercept strategies are proposed for the defense-intrusion game, introduced here as Attackeroriented and Neutral-position-oriented. Theoretical analysis and simulation results show that: (1) The two strategies are able to generate different synchronous intercept patterns: contact intercept pattern and stable non-contact intercept pattern, respectively. (2) The contact intercept pattern allows the defender robots to intercept the attacker robot in finite time, while the stable non-contact intercept pattern generates a periodic attractor that prevents the attack robot from reaching the target for infinite time. There is potential to apply the insights obtained into defense-intrusion in real systems, including aircraft escort and the defense of military targets or territorial boundaries.

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A Self-organizing Cooperative Hunting by Swarm Robotic Systems Based on Loose-preference Rule

Cited by 18 publications

References 11 publications

A novel bio- inspired algorithm for hunting in multi robot scenario

A novel bio- inspired algorithm for hunting in multi robot scenario

Reinforcement learning method for target hunting control of multi‐robot systems with obstacles

Synchronous intercept strategies for a robotic defense-intrusion game with two defenders

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