Academic Journal
Path Planning for Multiple Mobile Robots: A Systematic Review Using Parameter-Mapped Benchmarking.
| Title: | Path Planning for Multiple Mobile Robots: A Systematic Review Using Parameter-Mapped Benchmarking. |
|---|---|
| Authors: | Umbarkar, Ashish, Patle, Bhumeshwar K., Sanap, Sudarshan, Patel, Brijesh |
| Source: | Machines; Aug2026, Vol. 14 Issue 8, p870, 73p |
| Subject Terms: | Robotic path planning, Mobile robots, Deep learning, Multiagent systems, Benchmark problems (Computer science), Metaheuristic algorithms, Benchmark testing (Engineering), Algorithms |
| Abstract: | This survey presents a large-scale, reproducible, and parameter-mapped benchmarking analysis of path planning algorithms for multiple mobile robot systems (MMRS) by systematically examining 247 rigorously filtered papers from high-impact journals. Unlike prior reviews that primarily provide conceptual taxonomies, this survey introduces execution-oriented multi-parameter mapping enabling direct comparison of classical planners (A*, D*, Cell Decomposition, APF, RM, RRT, and ORCA), nature-inspired metaheuristics (PSO, GA, ACO, GWO, FA, ABC, BFO, CS, BA, SFLA, eagle-inspired optimizers), and learning-driven AI frameworks (Fuzzy Logic, Artificial Neural Networks, and Deep Reinforcement Learning). Each paper is evaluated across 15 practical planning dimensions, including environment type (static 95% vs. dynamic 51%), multi-robot validation (52%), dynamic goal handling (13%), energy awareness (14%), timepath optimization bias (82% focus), inter-robot coordination (less than 47%), and software validation platforms (MATLAB 42% and ROS 9%), revealing that simulation-only validation dominates (98%) while experimental testing remains limited (33%). Multivariate validation through Multiple Correspondence Analysis further confirms that coordination maturity, energy awareness, and multi-robot applicability are the primary structural differentiators of deployment readiness across algorithm families. The findings emphasize the need for hybrid, energy-aware, and coordination-driven MRPP frameworks supported by experimental benchmarking and reproducible deployment pipelines to advance real-world MMRS autonomy. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Path Planning for Multiple Mobile Robots: A Systematic Review Using Parameter-Mapped Benchmarking. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Umbarkar%2C+Ashish%22">Umbarkar, Ashish</searchLink><br /><searchLink fieldCode="AR" term="%22Patle%2C+Bhumeshwar+K%2E%22">Patle, Bhumeshwar K.</searchLink><br /><searchLink fieldCode="AR" term="%22Sanap%2C+Sudarshan%22">Sanap, Sudarshan</searchLink><br /><searchLink fieldCode="AR" term="%22Patel%2C+Brijesh%22">Patel, Brijesh</searchLink> – Name: TitleSource Label: Source Group: Src Data: Machines; Aug2026, Vol. 14 Issue 8, p870, 73p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Robotic+path+planning%22">Robotic path planning</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+robots%22">Mobile robots</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Multiagent+systems%22">Multiagent systems</searchLink><br /><searchLink fieldCode="DE" term="%22Benchmark+problems+%28Computer+science%29%22">Benchmark problems (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Benchmark+testing+%28Engineering%29%22">Benchmark testing (Engineering)</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This survey presents a large-scale, reproducible, and parameter-mapped benchmarking analysis of path planning algorithms for multiple mobile robot systems (MMRS) by systematically examining 247 rigorously filtered papers from high-impact journals. Unlike prior reviews that primarily provide conceptual taxonomies, this survey introduces execution-oriented multi-parameter mapping enabling direct comparison of classical planners (A*, D*, Cell Decomposition, APF, RM, RRT, and ORCA), nature-inspired metaheuristics (PSO, GA, ACO, GWO, FA, ABC, BFO, CS, BA, SFLA, eagle-inspired optimizers), and learning-driven AI frameworks (Fuzzy Logic, Artificial Neural Networks, and Deep Reinforcement Learning). Each paper is evaluated across 15 practical planning dimensions, including environment type (static 95% vs. dynamic 51%), multi-robot validation (52%), dynamic goal handling (13%), energy awareness (14%), timepath optimization bias (82% focus), inter-robot coordination (less than 47%), and software validation platforms (MATLAB 42% and ROS 9%), revealing that simulation-only validation dominates (98%) while experimental testing remains limited (33%). Multivariate validation through Multiple Correspondence Analysis further confirms that coordination maturity, energy awareness, and multi-robot applicability are the primary structural differentiators of deployment readiness across algorithm families. The findings emphasize the need for hybrid, energy-aware, and coordination-driven MRPP frameworks supported by experimental benchmarking and reproducible deployment pipelines to advance real-world MMRS autonomy. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Machines is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/machines14080870 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 73 StartPage: 870 Subjects: – SubjectFull: Robotic path planning Type: general – SubjectFull: Mobile robots Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Multiagent systems Type: general – SubjectFull: Benchmark problems (Computer science) Type: general – SubjectFull: Metaheuristic algorithms Type: general – SubjectFull: Benchmark testing (Engineering) Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: Path Planning for Multiple Mobile Robots: A Systematic Review Using Parameter-Mapped Benchmarking. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Umbarkar, Ashish – PersonEntity: Name: NameFull: Patle, Bhumeshwar K. – PersonEntity: Name: NameFull: Sanap, Sudarshan – PersonEntity: Name: NameFull: Patel, Brijesh IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20751702 Numbering: – Type: volume Value: 14 – Type: issue Value: 8 Titles: – TitleFull: Machines Type: main |
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