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Cited by 168Metaheuristic Optimization Algorithms ResearchRobotic Path Planning AlgorithmsModular Robots and Swarm Intelligence

Multi-robot path planning using improved particle swarm optimization algorithm through novel evolutionary operators

Das, P.K., Jena, P.K.·2020·Applied Soft Computing
DOI 10.1016/j.asoc.2020.106312
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Crossref · 10.1016/j.asoc.2020.106312

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  • The path trajectory formation process consists of four separate modules: perception, localisation, path planning, and motion control.

    “The path trajectory formation process consists of these four separate modules: perception, when the robot receives the necessary information from the sensors; localisation, when the robot aims to control its position in the environment; path planning; and motion control [79].”

    From Advanced Applications of Industrial Robotics: New Trends and Possibilities (2021) · cites this paper as [79]

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Authors
Das, P. K., & Jena, P. K.
Affiliations
Veer Surendra Sai University of Technology (IN)
Corresponding author
Das, P.K.
ORCID
Venue
Applied Soft Computing
Publisher
Elsevier BV
Published
2020-07
Volume
92
Pages
106312
ISSN
1568-4946 (ISSN-L)
DOI
10.1016/j.asoc.2020.106312
OpenAlex
W3018311626
Cited by
168
References
0
Citations by year
2026: 15 · 2025: 24 · 2024: 36 · 2023: 34 · 2022: 30 · 2021: 23 · 2020: 6
FWCI
8.99
Type
journal article
Language
en
Open access
closed
Licence
Locations
Applied Soft Computing (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
  • Physical Sciences › Engineering › Mechanical Engineering › Modular Robots and Swarm Intelligence
Keywords
multi-robot path planning, particle swarm optimization, evolutionary operators, collision-free paths, path length minimization, intensification and diversification, swarm intelligence, differential evolution comparison

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