Flocking for Multiple Subgroups of Multi-Agents With Different Social Distancing

Hui, Wei; Chen, Xuebo

doi:10.1109/access.2020.3022395

Cited by 13 publications

(7 citation statements)

References 30 publications

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“…Deep learning has an essential impact on tools used for social distancing; several pre-trained models used for this purpose have shown its efficiency in social distance measurement as person detection can be achieved through pre-trained model [1] , [23] , [24] , [25] . Hardware based approaches for mobile crowd sensing using several schemes like smart phone applications and IoT (Internet of Things) devices were used to detect social distancing and crowd control [19] , [26] , [27] , [28] , [29] , [30] . P-dispersion problem was proposed in [31] that can solve the minimum number of persons in a place problem, however it covers only one entity of the social distancing that’s number of persons in an area.…”

Section: Related Workmentioning

confidence: 99%

ViDMASK dataset for face mask detection with social distance measurement

et al. 2022

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Section: Related Workmentioning

confidence: 99%

ViDMASK dataset for face mask detection with social distance measurement

et al. 2022

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“…The complexity in the design of these systems lies in the fact that they can produce a system that is unable to solve the task either because the number of robots is too high or too low, if the local signal being tracked is too strong for the population size, or if the navigation environment is too complex [21]. These types of problems can drive the system to local minima, or impede the performance of the task.…”

Section: Introductionmentioning

confidence: 99%

A Software Framework for Self-Organized Flocking System Motion Coordination Research

Sarmiento¹,

Ariza²,

Jacinto³

2022

IJACSA

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We describe and analyze the basic algorithms for the self-organization of a swarm of robots in coordinated motion as a flock of agents as a strategy for the solution of multi-agent tasks. This analysis allows us to postulate a simulation framework for such systems based on the behavioral rules that characterize the dynamics of these systems. The problem is approached from the perspective of autonomous navigation in an unknown but restricted and locally observable environment. The simulation framework allows defining individually the characteristics of the basic behaviors identified as fundamental to show a flocking behavior, as well as the specific characteristics of the navigation environment. It also allows the incorporation of different path planning approaches to enable the system to navigate the environment for different strategies, both geometric and reactive. The basic behaviors modeled include safe wandering, following, aggregation, dispersion, and homing, which interact to generate flocking behavior, i.e., the swarm aggregates, reach a stable formation and move in an organized fashion toward the target point. The framework concept follows the principle of constrained target tracking, which allows the problem to be solved similarly as a small robot with limited computation would solve it. It is shown that the algorithm and the framework that implements it are robust to the defined constraints and manage to generate the flocking behavior while accomplishing the navigation task. These results provide key guidelines for the implementation of these algorithms on real platforms.

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“…A controller is an essential structure that is used in the implementation and simulation of swarm systems [15]. There are three main approaches to formation control systems [4,12,16]: the leader-follower approach [12,[17][18][19], the virtual structure approach [1,20,21], and the behavior-based approach [5,14,16,[22][23][24][25][26]. Despite the simplicity of the leader-follower approach, it may suffer from being highly dependent on the leader agent and from the large amount of information exchange required between the leader and each of the followers due to being a centralized system [4].…”

Section: Introductionmentioning

confidence: 99%

“…Then, the collisions between agent i ∈ n and obstacles at step s are computed, as in Equations ( 16)- (18). Finally, the average number of collisions among the flock members and with obstacles is computed, as in Equation (19).…”

mentioning

confidence: 99%

Effect of Formation Size on Flocking Formation Performance for the Goal Reach Problem

et al. 2022

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Flocking is one of the swarm tasks inspired by animal behavior. A flock involves multiple agents aiming to achieve a goal while maintaining certain characteristics of their formation. In nature, flocks vary in size. Although several studies have focused on the flock controller itself, less research has focused on how the flock size affects flock formation and performance. In this study, we address this problem and develop a simple flock controller for goal-zone-reaching tasks. The developed controller is intended for a two-dimensional environment and can handle obstacles as well as integrate an additional invented feature, called sensing power, in order to simulate the natural dynamics of migratory birds. This controller is simulated using the NetLogo simulation tool. Several experiments were conducted with and without obstacles, accompanied by changes in the flock size. The simulation results demonstrate that the flock controller is able to successfully deliver the flock to the goal zone. In addition, changes in the flock size affect multiple metrics, such as the time required to reach the goal (and, consequently, the time required to complete the flocking task), as well as the number of collisions that occur.

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Flocking for Multiple Subgroups of Multi-Agents With Different Social Distancing

Cited by 13 publications

References 30 publications

ViDMASK dataset for face mask detection with social distance measurement

ViDMASK dataset for face mask detection with social distance measurement

A Software Framework for Self-Organized Flocking System Motion Coordination Research

Effect of Formation Size on Flocking Formation Performance for the Goal Reach Problem

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