Formation Control of Swarm Robots Using Mobile Agents

Kambayashi, Yahiko; Yajima, Hideaki; Shyoji, Tadashi; Oikawa, Ryotaro; Takimoto, Munehiro

doi:10.1142/s2196888819500131

Cited by 10 publications

(7 citation statements)

References 36 publications

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“…to avoid placing fragile packages in unsafe locations) thereby allowing the human operator to better manage a set of such UAVs. The example can be extended, in a multi-agent environment, where UAVs can be organized in swarms [84] and modeled as cooperative agents to achieve more than what they could do solely, and the XAI system could explain this to the human operator for the sake of transparency, control, or for the sake of training novice operators on the system.…”

Section: Descriptionmentioning

confidence: 99%

The quest of parsimonious XAI: A human-agent architecture for explanation formulation

Mualla

Tchappi

Kampik

et al. 2022

Artificial Intelligence

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Section: Descriptionmentioning

confidence: 99%

The quest of parsimonious XAI: A human-agent architecture for explanation formulation

Mualla

Tchappi

Kampik

et al. 2022

Artificial Intelligence

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“…In ref. [108], a bio-inspired formation control algorithm for swarm mobile robots is presented based on ant agents (AA) and pheromone agents (PA). The communication is based on migration of PAs generated by the other AAs to drive and attract robots to compose the desired objective formation.…”

Section: Navigation In Formationmentioning

confidence: 99%

Mobile robots path planning and mobile multirobots control: A review

Hichri

Gallala

Giovannini³

et al. 2022

Robotica

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Mobile robots and multimobile robotic system usage for task achievement have been an emerging research area since the last decades. This article presents a review about mobile robot navigation problem and multimobile robotic systems control. The main focus is made on path planning strategies and algorithms in static and dynamic environments. A classification on mobile robots path planning has been defined in the literature and divided to classical and heuristic approaches. Each of them has its own advantages and drawbacks. On the other hand, the control of multimobile robots is presented and the control approaches for a fleet of robots are presented. Scientists found that using more than one robot as opposed to a single one presents many advantages when considering redundant task, dangerous tasks, or a task that scales up or down in time or that requires flexibility. They have defined three main approaches of multiple robots control: behavior-based approach, leader–follower approach, and virtual structure approach. This article addresses these approaches and provides examples from the literature.

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“…This research focuses on the development of multi-agent navigation strategies in this type of environment, guided by the identification of regions of interest in the environment [25]. These strategies must be following the task to be solved, so parameters such as system size, the distance between agents, the scope of the communication system, and characteristics of the basic behaviors must be different in each case [26]. Some assumptions are also made to simplify the model, but without moving it functionally away from the real prototypes.…”

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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Formation Control of Swarm Robots Using Mobile Agents

Cited by 10 publications

References 36 publications

The quest of parsimonious XAI: A human-agent architecture for explanation formulation

The quest of parsimonious XAI: A human-agent architecture for explanation formulation

Mobile robots path planning and mobile multirobots control: A review

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

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