Efficient Path Planning in Multi-Agent Environment of AAVs With Payloads
The development of Autonomous Aerial Vehicle (AAV) technology is considered promising for applications such as product delivery, surveillance, and search and rescue operations. However, efficient navigation through unknown and dynamic environments is one of the major challenges for such systems. Esp...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10938620/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850264929952595968 |
|---|---|
| author | Annapurna Jonnalagadda Yuva Sai Verma M. V. Bharat E. Z. Ushus |
| author_facet | Annapurna Jonnalagadda Yuva Sai Verma M. V. Bharat E. Z. Ushus |
| author_sort | Annapurna Jonnalagadda |
| collection | DOAJ |
| description | The development of Autonomous Aerial Vehicle (AAV) technology is considered promising for applications such as product delivery, surveillance, and search and rescue operations. However, efficient navigation through unknown and dynamic environments is one of the major challenges for such systems. Especially when AAVs are armed with payloads, one needs to consider additional factors like capacity, weight, fuel consumption, etc, which further fosters the complexity of the problem. This research proposes a multi-path framework to improve the intelligence-of-path planning algorithm for AAVs while navigating through new terrain to achieve their missions successfully. The proposed framework integrates a Constrained A* (CA*) algorithm for path planning, a Constraint Tree (CT), and a Conflict Avoidance Table (CAT) for resolving conflicts. Integrating conflict avoidance principles enables intelligent and proactive AAV behavior, allowing for the wide deployment of multi-AAV systems in various operational scenarios. This model adapts dynamic path adjustments based on real-time environmental data and predictive modeling to suit AAV trajectories under changing conditions. The performance evaluation demonstrates that the proposed model surpasses the state-of-the-art algorithms, especially in unknown environments. |
| format | Article |
| id | doaj-art-4f67824b7dcc44dcbbd500d33de10325 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4f67824b7dcc44dcbbd500d33de103252025-08-20T01:54:34ZengIEEEIEEE Access2169-35362025-01-0113579325794210.1109/ACCESS.2025.355454610938620Efficient Path Planning in Multi-Agent Environment of AAVs With PayloadsAnnapurna Jonnalagadda0https://orcid.org/0000-0002-4698-7707Yuva Sai Verma1M. V. Bharat2E. Z. Ushus3https://orcid.org/0000-0002-7093-7347School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IndiaThe development of Autonomous Aerial Vehicle (AAV) technology is considered promising for applications such as product delivery, surveillance, and search and rescue operations. However, efficient navigation through unknown and dynamic environments is one of the major challenges for such systems. Especially when AAVs are armed with payloads, one needs to consider additional factors like capacity, weight, fuel consumption, etc, which further fosters the complexity of the problem. This research proposes a multi-path framework to improve the intelligence-of-path planning algorithm for AAVs while navigating through new terrain to achieve their missions successfully. The proposed framework integrates a Constrained A* (CA*) algorithm for path planning, a Constraint Tree (CT), and a Conflict Avoidance Table (CAT) for resolving conflicts. Integrating conflict avoidance principles enables intelligent and proactive AAV behavior, allowing for the wide deployment of multi-AAV systems in various operational scenarios. This model adapts dynamic path adjustments based on real-time environmental data and predictive modeling to suit AAV trajectories under changing conditions. The performance evaluation demonstrates that the proposed model surpasses the state-of-the-art algorithms, especially in unknown environments.https://ieeexplore.ieee.org/document/10938620/A-star algorithm (A*)constraint tree (CT)conflict avoidance table (CA table)greedy best first search (GBFS)autonomous aerial vehicle (AAV) |
| spellingShingle | Annapurna Jonnalagadda Yuva Sai Verma M. V. Bharat E. Z. Ushus Efficient Path Planning in Multi-Agent Environment of AAVs With Payloads IEEE Access A-star algorithm (A*) constraint tree (CT) conflict avoidance table (CA table) greedy best first search (GBFS) autonomous aerial vehicle (AAV) |
| title | Efficient Path Planning in Multi-Agent Environment of AAVs With Payloads |
| title_full | Efficient Path Planning in Multi-Agent Environment of AAVs With Payloads |
| title_fullStr | Efficient Path Planning in Multi-Agent Environment of AAVs With Payloads |
| title_full_unstemmed | Efficient Path Planning in Multi-Agent Environment of AAVs With Payloads |
| title_short | Efficient Path Planning in Multi-Agent Environment of AAVs With Payloads |
| title_sort | efficient path planning in multi agent environment of aavs with payloads |
| topic | A-star algorithm (A*) constraint tree (CT) conflict avoidance table (CA table) greedy best first search (GBFS) autonomous aerial vehicle (AAV) |
| url | https://ieeexplore.ieee.org/document/10938620/ |
| work_keys_str_mv | AT annapurnajonnalagadda efficientpathplanninginmultiagentenvironmentofaavswithpayloads AT yuvasaiverma efficientpathplanninginmultiagentenvironmentofaavswithpayloads AT mvbharat efficientpathplanninginmultiagentenvironmentofaavswithpayloads AT ezushus efficientpathplanninginmultiagentenvironmentofaavswithpayloads |