A jump point search improved ant colony hybrid optimization algorithm for path planning of mobile robot Article Swipe
Tao Chen
,
Suifan Chen
,
Kuoran Zhang
,
Guoting Qiu
,
Qipeng Li
,
Xinmin Chen
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1177/17298806221127953
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1177/17298806221127953
To improve the finding path accuracy of the ant colony algorithm and reduce the number of turns, a jump point search improved ant colony optimization hybrid algorithm has been proposed in this article. Firstly, the initial pheromone concentration distribution gets from the jump points has been introduced to guide the algorithm in finding the way, thus accelerating the early iteration speed. The turning cost factor in the heuristic function has been designed to improve the smoothness of the path. Finally, the adaptive reward and punishment factor, and the Max–Min ant system have been introduced to improve the iterative speed and global search ability of the algorithm. Simulation through three maps of different scales is carried out. Furthermore, the results prove that the jump point search improved ant colony optimization hybrid algorithm performs effectively in finding path accuracy and reducing the number of turns.
Related Topics
Concepts
Ant colony optimization algorithms
Jump
Computer science
Path (computing)
Algorithm
Motion planning
Mathematical optimization
Smoothness
Heuristic
Point (geometry)
Convergence (economics)
Artificial intelligence
Robot
Mathematics
Mathematical analysis
Programming language
Economics
Economic growth
Geometry
Quantum mechanics
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/17298806221127953
- OA Status
- gold
- Cited By
- 24
- References
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4306175673
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4306175673Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1177/17298806221127953Digital Object Identifier
- Title
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A jump point search improved ant colony hybrid optimization algorithm for path planning of mobile robotWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-09-01Full publication date if available
- Authors
-
Tao Chen, Suifan Chen, Kuoran Zhang, Guoting Qiu, Qipeng Li, Xinmin ChenList of authors in order
- Landing page
-
https://doi.org/10.1177/17298806221127953Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1177/17298806221127953Direct OA link when available
- Concepts
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Ant colony optimization algorithms, Jump, Computer science, Path (computing), Algorithm, Motion planning, Mathematical optimization, Smoothness, Heuristic, Point (geometry), Convergence (economics), Artificial intelligence, Robot, Mathematics, Mathematical analysis, Programming language, Economics, Economic growth, Geometry, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
24Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 9, 2023: 9Per-year citation counts (last 5 years)
- References (count)
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14Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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