Flow Field-Based Enhanced Artificial Potential Field Method: Enhancing Cooperative Search and Obstacle Avoidance in Swarm Robotic Article Swipe
Liwei Xuan
,
Mingyong Liu
,
GuoYuan He
,
Zhiqiang Yan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4838779/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4838779/v1
In the context of cooperative search and obstacle avoidance for a swarm of robots using heuristic optimization algorithms, the traditional artificial potential field method often leads to oscillating robot trajectories and excessive dispersion of the robot swarm. To address these issues, this study proposes an improved artificial potential field method based on a flow field as the obstacle avoidance approach for cooperative search in a swarm of robots. This method incorporates the robot's safety range and dynamically adjusts the repulsive potential field of obstacles to reduce the negative impact of inefficient obstacle avoidance on search efficiency. Additionally, an adaptive inertia weight adjustment method is designed for the robots, which considers the essence of robot updates and aims to smooth the robot's search trajectory and promote consensus building within the robot swarm. Finally, the proposed algorithm is validated through simulations and compared with relevant algorithms in different environments to demonstrate its effectiveness and efficiency.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://doi.org/10.21203/rs.3.rs-4838779/v1
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402298867
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402298867Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-4838779/v1Digital Object Identifier
- Title
-
Flow Field-Based Enhanced Artificial Potential Field Method: Enhancing Cooperative Search and Obstacle Avoidance in Swarm RoboticWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-09-06Full publication date if available
- Authors
-
Liwei Xuan, Mingyong Liu, GuoYuan He, Zhiqiang YanList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-4838779/v1Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.21203/rs.3.rs-4838779/v1Direct OA link when available
- Concepts
-
Obstacle avoidance, Swarm behaviour, Field (mathematics), Potential field, Obstacle, Collision avoidance, Swarm robotics, Computer science, Artificial intelligence, Robot, Physics, Mathematics, Mobile robot, Computer security, Geography, Collision, Archaeology, Pure mathematics, GeophysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.algorithms | 144 |
| abstract_inverted_index.artificial | 21, 47 |
| abstract_inverted_index.dispersion | 33 |
| abstract_inverted_index.trajectory | 123 |
| abstract_inverted_index.algorithms, | 18 |
| abstract_inverted_index.cooperative | 5, 62 |
| abstract_inverted_index.demonstrate | 149 |
| abstract_inverted_index.dynamically | 77 |
| abstract_inverted_index.efficiency. | 96, 153 |
| abstract_inverted_index.inefficient | 91 |
| abstract_inverted_index.oscillating | 28 |
| abstract_inverted_index.simulations | 139 |
| abstract_inverted_index.traditional | 20 |
| abstract_inverted_index.environments | 147 |
| abstract_inverted_index.incorporates | 71 |
| abstract_inverted_index.optimization | 17 |
| abstract_inverted_index.trajectories | 30 |
| abstract_inverted_index.Additionally, | 97 |
| abstract_inverted_index.effectiveness | 151 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.17876614 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |