Synchronous intercept strategies for a robotic defense-intrusion game with two defenders
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Synchronous intercept strategies for a robotic defense-intrusion game with two defenders Shuai Zhang1
· Mingyong Liu1 · Xiaokang Lei2,3 · Panpan Yang4 · Yunke Huang1 · Ruaridh Clark5
Received: 8 July 2019 / Accepted: 26 August 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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 Attacker-oriented 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. Keywords Pursuit-evasion · Defense-intrusion · Synchronous intercept · Multi-robot systems
1 Introduction Pursuit-evasion phenomena is widely observed in nature, an example of which is the interaction between coyotes, elk and wolves in Yellowstone National Park (Ripple and Larsen 2000; Gese 2001). Studying this phenomena provides an insight into natural interactions, such as prey escape strategies (Breakwell 1975; Bhattacharya et al. 2011, 2014; Yang et al. 2014; Zha et al. 2016; Zhang et al. 2019), collective
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Shuai Zhang [email protected]
1
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, Shaanxi, People’s Republic of China
2
School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, Shaanxi, People’s Republic of China
3
KLINNS Lab, Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China
4
School of Electronic and Control Engineering, Chang’an University, Xi’an, China
5
Department of Mechanical and Aerospace Engineering, University of Strathclyde, Glasgow, UK
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 au
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