Dependence of Dynamics of Multi-robot System on Control Architecture

Serebrenny, Vladimir; Shereuzhev, Madin

doi:10.1007/978-3-030-37841-7_10

Cited by 8 publications

(3 citation statements)

References 2 publications

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“…The vector β is the seeking vector which is expressed in equation (9). ψ is the amount of the global directions whither the R k a will seek for the prey.…”

Section: Figure 2 the Antagonist Limits The Area Seeking For The Targetmentioning

confidence: 99%

“…Furthermore, the hunting and pursuit tasks need analyzing of the situation and information available in real time by all the robots within the system, and sharing useful information between them [6]. Hence, this is a multidisciplinary task which requires knowledge in robotics, optimization algorithms, real-time dynamic path planning [7], wireless communication [8], management of MRS [9], etc. Therefore, it is a research and optimization problem; as it is a problem in order to deal with dynamic information that is constantly changing, and to find the goal through cooperation and coordination between robots, as well as trapping the target with the minimum time and resources possible.…”

Section: Introductionmentioning

confidence: 99%

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Improvised multi-robot cooperation strategy for hunting a dynamic target

Hamed¹,

Hamlich²

2021

EAI Endorsed Trans IoT

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Hunting moving targets with random motions and behavior is a challenge for robotic systems, and it occupies a significant position in the research on coordination and cooperation in multi-robot systems. Cooperative hunting between robots is used in a wide range of fields such as industry, military, rescue and other fields. The aim of this paper is to present a new strategy for target hunting by means of a cooperative multi-robot system in two-dimensional space, especially moving targets with random and unexpected behavior. The strategy is inspired from the behavior of wolves in hunting and Wolf Swarm Algorithm, as it summarizes the roles of robots in the system in three roles: the leader wolf, the antagonist wolf, and the follower wolf. This diversity of roles contributed to improve the convergence performance of the algorithm and reduce significantly the pursuit time. The validity of this strategy is supported by computer simulations.

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“…The vector β is the seeking vector which is expressed in equation (9). ψ is the amount of the global directions whither the R k a will seek for the prey.…”

Section: Figure 2 the Antagonist Limits The Area Seeking For The Targetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improvised multi-robot cooperation strategy for hunting a dynamic target

Hamed¹,

Hamlich²

2021

EAI Endorsed Trans IoT

View full text Add to dashboard Cite

show abstract

“…Recently, numerous articles have focused on the multiagent robotics systems (Afanasyev et al, 2019;Akhtar & Missen, 2015;Chen et al, 2010;Li et al, 2019;Liu & Wu, 2018;Nazarova & Zhai, 2019;Serebrenny & Shereuzhev, 2020;Tang et al, 2015;Vorotnikov et al, 2018;Wu et al, 2018). A set of principles for multi-agent robotic systems control has been described in Vorotnikov et al (2018).…”

Section: Introductionmentioning

confidence: 99%

Fast terminal sliding mode control for a nonlinear multi-agent robot system with disturbance

Naserian

Ramazani

Khaki‐Sedigh

et al. 2020

Systems Science & Control Engineering

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This paper addresses the consensus control problem for a nonlinear multi-agent robot system in the presence of external disturbances. Robot system is one of the most important practical systems in the industry. Because of the presence of disturbances in most practical systems, this paper considers the issue of finite-time consensus control of the nonlinear multi-agent robot system in the presence of unknown bounded disturbance. The modified terminal sliding mode control method is suggested for the system which is able to guarantee the stability of the overall system and fast finite-time consensus control purpose. In two different scenarios, the simulation of multi-agent robot system has been performed. The results show the effectiveness of the proposed control method on the multi-agent robot system.

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Multi-criteria Optimization of the Mobile Robot Group Strategy Using the Ant Algorithm

Darintsev

Airat

2021

Electromechanics and Robotics

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One of the problems of group control, the distribution, of tasks in a group of mobile robots, is considered. The basic solution algorithm is proposed to use the "swarm intelligence" implemented on the basis of ant algorithms. This article is a development of the previous authors' work, which reviewed the known approaches to the distribution of problems using swarm algorithms, described the ant algorithm in detail, and analyzed the resulting solution to the problem of single-criteria optimization. The problem statement, the working space model are presented, the robot function objectives and parameters characterizing their work are formalized. To solve the multi-criteria problem, the following quality criteria were selected: The amount of energy expended, the time it took to complete the tasks, and the number of robots involved. For multi-criteria optimization, the resulting vector optimality criterion is linearly converged by introducing additional parameters characterizing group control: The total efficiency of a group of robots, the specific amount of energy for the operation of the support group and the energy for moving one robot to the given coordinates. To implement the ant algorithm, the problem was presented in the form of a set of undirected weighted graphs on which the "ants" will build solutions. An example is given that shows a detailed transition from the initial problem setting to graphs. The experiments were carried out on the basis of a software-implemented algorithm for finding the optimal group behavior strategy for a multi-criteria objective function with weight coefficients. For a group of three robots, experimental data were obtained and the results analysed, and a solution was modeled for several groups of robots with different weights.

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Dependence of Dynamics of Multi-robot System on Control Architecture

Cited by 8 publications

References 2 publications

Improvised multi-robot cooperation strategy for hunting a dynamic target

Improvised multi-robot cooperation strategy for hunting a dynamic target

Fast terminal sliding mode control for a nonlinear multi-agent robot system with disturbance

Multi-criteria Optimization of the Mobile Robot Group Strategy Using the Ant Algorithm

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