Multi robot path planning and path coordination using genetic algorithms

Muthumeena Muthiah; Ashraf Saad

doi:10.1145/3077286.3077327

Multi robot path planning and path coordination using genetic algorithms

Muthumeena Muthiah, Ashraf Saad

Research output: Contribution to book or proceeding › Conference article › peer-review

5 Scopus citations

Abstract

Planning optimal paths for multiple robots is computationally expensive. In this research, we provide a Genetic Algorithm implementation for multi robot path planning. Path planning for multiple mobile robots must devise a collision-free path for each robot. The paper presents a Genetic Algorithm multi robot path planner that we developed to provide a solution to the problem. Experimental results using m3pi robots confirm the usefulness of the proposed solution in a variety of scenarios such as multi robot navigation as well as scenarios that require coordination of multiple robots to achieve a common goal such as pushing a box or trapping a prey.

Original language	English
Title of host publication	Proceedings of the SouthEast Conference, ACMSE 2017
Publisher	Association for Computing Machinery, Inc
Pages	112-119
Number of pages	8
ISBN (Electronic)	9781450350242
DOIs	https://doi.org/10.1145/3077286.3077327
State	Published - Apr 13 2017
Externally published	Yes
Event	2017 ACM SouthEast Regional Conference, ACMSE 2017 - Kennesaw, United States Duration: Apr 13 2017 → Apr 15 2017

Publication series

Name	Proceedings of the SouthEast Conference, ACMSE 2017

Conference

Conference	2017 ACM SouthEast Regional Conference, ACMSE 2017
Country/Territory	United States
City	Kennesaw
Period	04/13/17 → 04/15/17

Scopus Subject Areas

Software
Computational Theory and Mathematics
Hardware and Architecture
Computer Science Applications

Keywords

Collaborative tasks
Collision Avoidance
Communication
Genetic Algorithms
M3pi robots
Multi Robot
Path Coordination
Path Planning

Access to Document

10.1145/3077286.3077327

Cite this

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title = "Multi robot path planning and path coordination using genetic algorithms",

abstract = "Planning optimal paths for multiple robots is computationally expensive. In this research, we provide a Genetic Algorithm implementation for multi robot path planning. Path planning for multiple mobile robots must devise a collision-free path for each robot. The paper presents a Genetic Algorithm multi robot path planner that we developed to provide a solution to the problem. Experimental results using m3pi robots confirm the usefulness of the proposed solution in a variety of scenarios such as multi robot navigation as well as scenarios that require coordination of multiple robots to achieve a common goal such as pushing a box or trapping a prey.",

keywords = "Collaborative tasks, Collision Avoidance, Communication, Genetic Algorithms, M3pi robots, Multi Robot, Path Coordination, Path Planning",

author = "Muthumeena Muthiah and Ashraf Saad",

year = "2017",

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day = "13",

doi = "10.1145/3077286.3077327",

language = "English",

series = "Proceedings of the SouthEast Conference, ACMSE 2017",

publisher = "Association for Computing Machinery, Inc",

pages = "112--119",

booktitle = "Proceedings of the SouthEast Conference, ACMSE 2017",

}

Muthiah, M & Saad, A 2017, Multi robot path planning and path coordination using genetic algorithms. in Proceedings of the SouthEast Conference, ACMSE 2017. Proceedings of the SouthEast Conference, ACMSE 2017, Association for Computing Machinery, Inc, pp. 112-119, 2017 ACM SouthEast Regional Conference, ACMSE 2017, Kennesaw, United States, 04/13/17. https://doi.org/10.1145/3077286.3077327

TY - GEN

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AU - Muthiah, Muthumeena

AU - Saad, Ashraf

PY - 2017/4/13

Y1 - 2017/4/13

N2 - Planning optimal paths for multiple robots is computationally expensive. In this research, we provide a Genetic Algorithm implementation for multi robot path planning. Path planning for multiple mobile robots must devise a collision-free path for each robot. The paper presents a Genetic Algorithm multi robot path planner that we developed to provide a solution to the problem. Experimental results using m3pi robots confirm the usefulness of the proposed solution in a variety of scenarios such as multi robot navigation as well as scenarios that require coordination of multiple robots to achieve a common goal such as pushing a box or trapping a prey.

AB - Planning optimal paths for multiple robots is computationally expensive. In this research, we provide a Genetic Algorithm implementation for multi robot path planning. Path planning for multiple mobile robots must devise a collision-free path for each robot. The paper presents a Genetic Algorithm multi robot path planner that we developed to provide a solution to the problem. Experimental results using m3pi robots confirm the usefulness of the proposed solution in a variety of scenarios such as multi robot navigation as well as scenarios that require coordination of multiple robots to achieve a common goal such as pushing a box or trapping a prey.

KW - Collaborative tasks

KW - Collision Avoidance

KW - Communication

KW - Genetic Algorithms

KW - M3pi robots

KW - Multi Robot

KW - Path Coordination

KW - Path Planning

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U2 - 10.1145/3077286.3077327

DO - 10.1145/3077286.3077327

M3 - Conference article

AN - SCOPUS:85021410391

T3 - Proceedings of the SouthEast Conference, ACMSE 2017

SP - 112

EP - 119

BT - Proceedings of the SouthEast Conference, ACMSE 2017

PB - Association for Computing Machinery, Inc

T2 - 2017 ACM SouthEast Regional Conference, ACMSE 2017

Y2 - 13 April 2017 through 15 April 2017

ER -

Multi robot path planning and path coordination using genetic algorithms

Abstract

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Conference

Scopus Subject Areas

Keywords

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