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37554

MULTI-AGENT SYSTEM PATH-PLANNING USING AGENT PERCEPTUAL MODEL AND DYNAMIC ARTIFICIAL POTENTIAL FIELDS

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Last updated: 24 Dec 2024

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Abstract

Abstract Artificial teams of mobile robots, which are capable of doing difficult jobs with relatively lower cost, gain a growing interest in the robotic field. In this paper, a model is used for a second order non-linear system to simulate a group of agents that interact via pair-wise attractive and repulsive potentials. a new potential field method is proposed using individually based agent perceptual model to successfully solve the local minimum problem, which is considered the main drawback in the artificial potential field based path-planning approach. The work in this paper is considered an extension of my earlier work to overcome local minima using internal agent states [1]. Allowing the potential field to be dynamic and manipulated by the model can solve the problem even easier. Simulation results demonstrate the ability of a single agent as well as group of agents to solve the problem and reach the goal position effectively.

DOI

10.21608/amme.2010.37554

Keywords

Robotics, artificial potential field, path-planning

Authors

First Name

M.

Last Name

MABROUK

MiddleName

H.

Affiliation

Egyptian Armed Forces.

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Volume

14

Article Issue

14th International Conference on Applied Mechanics and Mechanical Engineering.

Related Issue

6057

Issue Date

2010-05-01

Receive Date

2019-06-27

Publish Date

2010-05-01

Page Start

1

Page End

12

Print ISSN

2636-4352

Online ISSN

2636-4360

Link

https://amme.journals.ekb.eg/article_37554.html

Detail API

https://amme.journals.ekb.eg/service?article_code=37554

Order

13

Type

Original Article

Type Code

831

Publication Type

Journal

Publication Title

The International Conference on Applied Mechanics and Mechanical Engineering

Publication Link

https://amme.journals.ekb.eg/

MainTitle

MULTI-AGENT SYSTEM PATH-PLANNING USING AGENT PERCEPTUAL MODEL AND DYNAMIC ARTIFICIAL POTENTIAL FIELDS

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Type

Article

Created At

22 Jan 2023