Cooperative capture by multi-agent using reinforcement learning application for security patrol systems

Shimada, Yasuhiro; Ohtsuka, Hirofumi; Matsumoto, Tadashi; Maya, Harada

doi:10.1109/ascc.2015.7244682

Cited by 8 publications

(5 citation statements)

References 1 publication

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“…Sugiyama et al [17] designed different visit frequencies for all purposes in the patrolling area and applied divisional cooperation to achieve the patrol tasks. Yasuyuki et al [18] applied reinforcement learning based on discrete patrolling areas to perform security patrol tasks.…”

Section: Related Workmentioning

confidence: 99%

“…PSO uses a fitness function to evaluate the quality of particles (line 16). The global optimal position of the particle swarm is updated iteratively, which is iterated until the maximum number of iterations (lines [17][18]. must return to the base station and replenish fuel, and then continue to visit the area to be visited.…”

Section: Patrolling Path Planning Algorithm Based On Improved Psomentioning

confidence: 99%

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Hybrid Partition-Based Patrolling Scheme for Maritime Area Patrol with Multiple Cooperative Unmanned Surface Vehicles

Xie

Zhou

Luo

et al. 2020

JMSE

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Multi-robot cooperative patrolling systems have been extensively employed in the civilian and military fields, including monitoring forest fires, marine search-and-rescue, and area patrol. Multi-robot area patrol problems refer to the activity that a team of robots works cooperatively and regularly to visit the key targets in the given area for security. Following consideration of the low cost and high safety of unmanned surface vehicles (USV), a team of USVs is organized to perform area patrol in a sophisticated maritime environment. In this paper, we establish a mathematical model considering the characteristics of the cooperative patrol task and the limited conditions of USVs. A hybrid partition-based patrolling scheme is proposed for a multi-USV system to visit targets with different importance levels in a maritime area. Firstly, a centralized area partition algorithm is utilized to partition the patrolling area according to the number of USVs. Secondly, a distributed path planning algorithm is applied to planning the patrolling path for each USV to visit the targets in a maritime environment to minimize the length of the patrolling path for the USV team. Finally, comparative experiments between the proposed scheme and other methods are carried out to validate the performance of the hybrid partition-based patrolling scheme. Simulation results and experimental analysis show the efficiency of the proposed hybrid partition-based patrolling scheme compared to several previous patrolling algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Patrolling Path Planning Algorithm Based On Improved Psomentioning

confidence: 99%

Hybrid Partition-Based Patrolling Scheme for Maritime Area Patrol with Multiple Cooperative Unmanned Surface Vehicles

Xie

Zhou

Luo

et al. 2020

JMSE

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“…The use of robots as a bodyguards is related to several different area of research and has received significant attention lately. Several different studies such as (Richard Klima, 2016;Yasuyuki et al, 2015) considered using robots and multi-agent reinforcement learning for security related tasks such as patrolling and team coordination by placing checkpoints to provide protection against imminent adversaries. A multi-robot patrolling framework was proposed by (Khan et al, 2012) that analyzes the behavior pattern of the soldiers and the robot and generates a patrolling schedule.…”

Section: Related Workmentioning

confidence: 99%

Designing a Multi-Objective Reward Function for Creating Teams of Robotic Bodyguards Using Deep Reinforcement Learning

Sheikh,

Bölöni

2019

Preprint

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We are considering a scenario where a team of bodyguard robots provides physical protection to a VIP in a crowded public space. We use deep reinforcement learning to learn the policy to be followed by the robots. As the robot bodyguards need to follow several difficult-to-reconcile goals, we study several primitive and composite reward functions and their impact on the overall behavior of the robotic bodyguards.

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“…[9] To solve the problem regarding the identification of the best strategy to enclose an intruder for pursuers and adopt the huge environment,SHIMADA Yasuyuki and OHTSUKA Hirofumi discussed how to discretize patrol areas based on reinforcement learning. [10] Francisco Martinez-Gil and Miguel Lozano compared the two different learning algorithms based on Reinforcement learning :Iterative Vector Quantization with Q-Learning (ITVQQL) TS, Tile coding as the generalization method with the Sarsa(TS). The simulation results for pedestrian showed that the two RL framework had common advantage that they could generate emergent collective behavior which can help them arrive their destination collaborative.…”

Section: Related Workmentioning

confidence: 99%

“…GetResponse(X) The coalitions are short-lived and goal-oriented [6].If a coalition lasts longer than the default requirement lif e, it will be considered as a failure. The algorithms shown in Algorithm 3 are explained in the following manner:The organizer initializes the neuron network(01).then the pursuers start to catch evaders under the coordination of organizers until all evaders are captured(02-33).The organizer broadcast position and value of each evader found in environment and wait the response from pursuers(03-06).The pursuers response with their own feature vector − → x p (07-09).The organizer train the SOM layer with the set of feature vectors X and send target and − −− → GAF to each pursuer after creating group p (10)(11)(12)(13)(14).The pursuer p response their CEF (p) and start get close to its target (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30).The organizer train its feature extraction part with − −− → CEF (31-32).The sequences diagram describing orginaser and pursuer communications is shown in Figure13.…”

mentioning

confidence: 99%

Coalition formation for Multi-agent Pursuit based on Neural Network and AGRMF Model

Pei,

Piao,

Souidi

2017

Preprint

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An approach for coalition formation of multi-agent pursuit based on neural network and AGRMF model is proposed.This paper constructs a novel neural work called AGRMF-ANN which consists of feature extraction part and group generation part. On one hand,The convolutional layers of feature extraction part can abstract the features of agent group role membership function(AGRMF) for all of the groups,on the other hand,those features will be fed to the group generation part based on self-organizing map(SOM) layer which is used to group the pursuers with similar features in the same group. Besides, we also come up the group attractiveness function(GAF) to evaluate the quality of groups and the pursuers contribution in order to adjust the main ability indicators of AGRMF and other weight of all neural network. The simulation experiment showed that this proposal can improve the effectiveness of coalition formation for multi-agent pursuit and ability to adopt pursuit-evasion problem with the scale of pursuer team growing.

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Cooperative capture by multi-agent using reinforcement learning application for security patrol systems

Cited by 8 publications

References 1 publication

Hybrid Partition-Based Patrolling Scheme for Maritime Area Patrol with Multiple Cooperative Unmanned Surface Vehicles

Hybrid Partition-Based Patrolling Scheme for Maritime Area Patrol with Multiple Cooperative Unmanned Surface Vehicles

Designing a Multi-Objective Reward Function for Creating Teams of Robotic Bodyguards Using Deep Reinforcement Learning

Coalition formation for Multi-agent Pursuit based on Neural Network and AGRMF Model

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