Cooperative Path Planning for Multiple UAVs Based on APF B-RRT* Algorithm Article Swipe
Cai-Yu Wu
,
Zhengyu Guo
,
J. Andrew Zhang
,
Kangle Mao
,
Delin Luo
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/drones9030177
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/drones9030177
Aiming at the path planning problem of an unmanned aerial vehicle (UAV) in a complex unknown environment, this paper proposes a cooperative path planning algorithm for multiple UAVs. Using the local environment information, several rolling path plannings are carried out by the Artificial Potential Field Bidirectional-Rapidly exploring Random Trees (APF B-RRT*) algorithm. The APF B-RRT* algorithm optimizes the search space by pre-sampling and adapts with an adaptive step while fusing with the APF algorithm for guiding sampling. Then, the generated path is trimmed and smoothed to obtain the optimized path. Then, through the sampling constraint, several paths can be planned at the same time, which are guaranteed not to collide. The model predictive control (MPC) is used to realize the cooperative control of the UAVs, that is, the UAVs reached the destination simultaneously along the planned path. This algorithm achieves some progress in solving the problems of slow convergence speed, an unstable result and an unsmooth path in UAV path planning. Simulation and comparison show that the APF B-RRT* algorithm has certain advantages in algorithm performance.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/drones9030177
- https://www.mdpi.com/2504-446X/9/3/177/pdf?version=1740661601
- OA Status
- gold
- Cited By
- 10
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408024823
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408024823Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/drones9030177Digital Object Identifier
- Title
-
Cooperative Path Planning for Multiple UAVs Based on APF B-RRT* AlgorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-02-27Full publication date if available
- Authors
-
Cai-Yu Wu, Zhengyu Guo, J. Andrew Zhang, Kangle Mao, Delin LuoList of authors in order
- Landing page
-
https://doi.org/10.3390/drones9030177Publisher landing page
- PDF URL
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https://www.mdpi.com/2504-446X/9/3/177/pdf?version=1740661601Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2504-446X/9/3/177/pdf?version=1740661601Direct OA link when available
- Concepts
-
Path (computing), Computer science, Motion planning, Algorithm, Artificial intelligence, Computer network, RobotTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 10Per-year citation counts (last 5 years)
- References (count)
-
17Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.result | 152 |
| abstract_inverted_index.search | 58 |
| abstract_inverted_index.speed, | 149 |
| abstract_inverted_index.B-RRT*) | 50 |
| abstract_inverted_index.carried | 38 |
| abstract_inverted_index.certain | 171 |
| abstract_inverted_index.complex | 14 |
| abstract_inverted_index.control | 113, 121 |
| abstract_inverted_index.guiding | 75 |
| abstract_inverted_index.planned | 99, 135 |
| abstract_inverted_index.problem | 5 |
| abstract_inverted_index.reached | 129 |
| abstract_inverted_index.realize | 118 |
| abstract_inverted_index.rolling | 34 |
| abstract_inverted_index.several | 33, 95 |
| abstract_inverted_index.solving | 143 |
| abstract_inverted_index.through | 91 |
| abstract_inverted_index.trimmed | 82 |
| abstract_inverted_index.unknown | 15 |
| abstract_inverted_index.vehicle | 10 |
| abstract_inverted_index.achieves | 139 |
| abstract_inverted_index.adaptive | 66 |
| abstract_inverted_index.collide. | 109 |
| abstract_inverted_index.multiple | 26 |
| abstract_inverted_index.planning | 4, 23 |
| abstract_inverted_index.problems | 145 |
| abstract_inverted_index.progress | 141 |
| abstract_inverted_index.proposes | 19 |
| abstract_inverted_index.sampling | 93 |
| abstract_inverted_index.smoothed | 84 |
| abstract_inverted_index.unmanned | 8 |
| abstract_inverted_index.unsmooth | 155 |
| abstract_inverted_index.unstable | 151 |
| abstract_inverted_index.Potential | 43 |
| abstract_inverted_index.algorithm | 24, 55, 73, 138, 169, 174 |
| abstract_inverted_index.exploring | 46 |
| abstract_inverted_index.generated | 79 |
| abstract_inverted_index.optimized | 88 |
| abstract_inverted_index.optimizes | 56 |
| abstract_inverted_index.planning. | 160 |
| abstract_inverted_index.plannings | 36 |
| abstract_inverted_index.sampling. | 76 |
| abstract_inverted_index.Artificial | 42 |
| abstract_inverted_index.Simulation | 161 |
| abstract_inverted_index.advantages | 172 |
| abstract_inverted_index.algorithm. | 51 |
| abstract_inverted_index.comparison | 163 |
| abstract_inverted_index.guaranteed | 106 |
| abstract_inverted_index.predictive | 112 |
| abstract_inverted_index.constraint, | 94 |
| abstract_inverted_index.convergence | 148 |
| abstract_inverted_index.cooperative | 21, 120 |
| abstract_inverted_index.destination | 131 |
| abstract_inverted_index.environment | 31 |
| abstract_inverted_index.environment, | 16 |
| abstract_inverted_index.information, | 32 |
| abstract_inverted_index.performance. | 175 |
| abstract_inverted_index.pre-sampling | 61 |
| abstract_inverted_index.simultaneously | 132 |
| abstract_inverted_index.Bidirectional-Rapidly | 45 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.99468384 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |