Cooperative Pursuit With Multi-Pursuer and One Faster Free-Moving Evader

Xu, Fang; Wang, Chen; Xie, Lihua; Chen, Jie

doi:10.1109/tcyb.2019.2958548

Cited by 60 publications

(13 citation statements)

References 31 publications

(40 reference statements)

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“…encirclement, ambushing, and relay running, by designing a variable inter-individual repulsion depending on the distance from the evader [19]. In [20], a surrounding force, which depends on the distance from neighbors, is introduced to evenly distribute pursuers on the encirclement to the evader. Although cooperation is achieved to some extent in the above works, it is extremely vulnerable to escape policies and initial conditions.…”

Section: Related Workmentioning

confidence: 99%

Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learning

Zhang¹,

Wang²,

Zhang³

et al. 2022

Preprint

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It is of great challenge, though promising, to coordinate collective robots for hunting an evader in a decentralized manner purely in light of local observations. In this paper, this challenge is addressed by a novel hybrid cooperative pursuit algorithm that combines reinforcement learning with the artificial potential field method. In the proposed algorithm, decentralized deep reinforcement learning is employed to learn cooperative pursuit policies that are adaptive to dynamic environments. The artificial potential field method is integrated into the learning process as predefined rules to improve the data efficiency and generalization ability. It is shown by numerical simulations that the proposed hybrid design outperforms the pursuit policies either learned from vanilla reinforcement learning or designed by the potential field method. Furthermore, experiments are conducted by transferring the learned pursuit policies into real-world mobile robots. Experimental results demonstrate the feasibility and potential of the proposed algorithm in learning multiple cooperative pursuit strategies.

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Section: Related Workmentioning

confidence: 99%

Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learning

Zhang¹,

Wang²,

Zhang³

et al. 2022

Preprint

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“…In other work [119]- [121], reach and avoid differential games were proposed for applications of aircraft control, motion planning, and collision avoidance. Environments in the presence of obstacles were also studied [122], while other authors [123] considered a multiple-pursuer and singleevader problem in which the multiple cooperative pursuers (i.e., pUAVs) capture a single evader (i.e., mUAV). A single-pursuer and multiple-evader problem was also studied [124], [125].…”

Section: B Sky Platformmentioning

confidence: 99%

Protect Your Sky: A Survey of Counter Unmanned Aerial Vehicle Systems

et al. 2020

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Recognizing the various and broad range of applications of unmanned aerial vehicles (UAVs) and unmanned aircraft systems (UAS) for personal, public and military applications, recent un-intentional malfunctions of uncontrollable UAVs or intentional attacks on them divert our attention and motivate us to devise a protection system, referred to as a counter UAV system (CUS). The CUS, also known as a counter-drone system, protects personal, commercial, public, and military facilities and areas from uncontrollable and belligerent UAVs by neutralizing or destroying them. This paper provides a comprehensive survey of the CUS to describe the key technologies of the CUS and provide sufficient information with wich to comprehend this system. The first part starts with an introduction of general UAVs and the concept of the CUS. In the second part, we provide an extensive survey of the CUS through a top-down approach: i) the platform of CUS including ground and sky platforms and related networks; ii) the architecture of the CUS consisting of sensing systems, command-and-control (C2) systems, and mitigation systems; and iii) the devices and functions with the sensors for detection-and-identification and localization-and-tracking actions and mitigators for neutralization. The last part is devoted to a survey of the CUS market with relevant challenges and future visions. From the CUS market survey, potential readers can identify the major players in a CUS industry and obtain information with which to develop the CUS industry. A broad understanding gained from the survey overall will assist with the design of a holistic CUS and inspire cross-domain research across physical layer designs in wireless communications, CUS network designs, control theory, mechanics, and computer science, to enhance counter UAV techniques further.INDEX TERMS Unmanned aerial vehicle (UAV), unmanned aircraft system (UAS), counter UAV system (CUS), counter drone systems, public safety, defense.

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“…Currently, research on multi-agent systems has advanced significantly, with the findings of studies demonstrating the benefits of multi-agent systems over single agent systems [1]- [4]. This multi-agent system has been widely used in a variety of fields, including target tracking [5], [6], formation control [7], [8], obstacle avoidance [9], [10], and other interesting research areas involving multi-agent systems, such as pursuit-evasion games or differential games [11]- [14].…”

Section: Introductionmentioning

confidence: 99%

Modified Extremum Seeking Control for Target Tracking and Formation Control in Pursuit-Evasion Game

Setiawan

Agustinah

Fuad

2022

JAREE

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In a pursuit-evasion game, the mobile robot pursuer's ability to navigate from its initial position to the evader while maintaining a safe distance from other objects requires a good obstacle avoidance system. This study aims to perform target tracking in evader sieges and obstacle avoidance against other pursuer robots and static obstacles by proposing a modified extreme seeking controller (ESC). A modified backstepping control (BC) was used as an autopilot control for a nonholonomic mobile robot to execute the modified ESC command. The modified BC based on the modified ESC requires the positions of the targeted evader, pursuers, and obstacles. The pursuer uses this information to capture an evader by arranging the desired formation without colliding with static obstacles or other robots. The results of the simulations show that the pursuers successfully surround the evader and construct the formation without colliding with obstacles. The proposed method resulted in the closest distance of 2.071 m between the pursuers, 1.954 m between each pursuer and the evader, and 2.425 m between the pursuers and static obstacles.

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Cooperative Pursuit With Multi-Pursuer and One Faster Free-Moving Evader

Cited by 60 publications

References 31 publications

Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learning

Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learning

Protect Your Sky: A Survey of Counter Unmanned Aerial Vehicle Systems

Modified Extremum Seeking Control for Target Tracking and Formation Control in Pursuit-Evasion Game

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