Butterfly magnetoreception based neighbour awareness strategy protocol for autonomous aerial vehicles
Abstract Autonomous Aerial Vehicles (AAVs) play a significant role in emergency response and disaster management, such as during forest fires, earthquakes, and tsunamis. However, navigating through high-density obstacle environments poses substantial challenges due to process overheads, dynamic envi...
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| Format: | Article |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-97283-x |
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| author | Janjhyam Venkata Naga Ramesh C. Dastagiraiah Suraya Mubeen W. Deva Priya M. Kameswara Rao B. H. K. Bhagat Kumar |
| author_facet | Janjhyam Venkata Naga Ramesh C. Dastagiraiah Suraya Mubeen W. Deva Priya M. Kameswara Rao B. H. K. Bhagat Kumar |
| author_sort | Janjhyam Venkata Naga Ramesh |
| collection | DOAJ |
| description | Abstract Autonomous Aerial Vehicles (AAVs) play a significant role in emergency response and disaster management, such as during forest fires, earthquakes, and tsunamis. However, navigating through high-density obstacle environments poses substantial challenges due to process overheads, dynamic environmental conditions, and scalability issues. A novel Neighbour Awareness Strategy (NAS) protocol is proposed to address these challenges, focusing on efficient obstacle avoidance while maintaining safety, adaptability, and reactiveness. NAS protocol integrates Butterfly Magnetoreception Mechanism (BMM) and Machine Learning (ML) algorithms. BMM enhances navigational safety by reducing congestion and minimising decision-making delays during real-time events. ML algorithms ensure optimal energy consumption and risk mitigation by dynamically selecting the shortest path, even under abrupt environmental changes. Key navigation parameters, including orientation angle (θ), heading angle (α), and velocity vector (V), are used to achieve precise and adaptive control. Simulation results demonstrate that NAS outperforms existing methods, such as Adaptive Path planning with Dynamic Obstacle Avoidance (APA-DOA) and Real-time Environment Adaptive Trajectory Planning (REAT). It achieves superior obstacle avoidance, faster reaction times, and enhanced operational efficiency. Overall, the proposed NAS protocol enables AAVs to maintain optimal orientation during flight, adjust heading based on environmental inputs, coordinate swarm movements, and navigate complex three-dimensional spaces effectively. |
| format | Article |
| id | doaj-art-41b4c7f22bd046dca7277b322ea51816 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-41b4c7f22bd046dca7277b322ea518162025-08-20T02:24:29ZengNature PortfolioScientific Reports2045-23222025-04-0115111410.1038/s41598-025-97283-xButterfly magnetoreception based neighbour awareness strategy protocol for autonomous aerial vehiclesJanjhyam Venkata Naga Ramesh0C. Dastagiraiah1Suraya Mubeen2W. Deva Priya3M. Kameswara Rao4B. H. K. Bhagat Kumar5Department of CSE, Graphic Era Hill UniversitySchool of Engineering, Department of CSE, Anurag UniversityDepartment of ECE, CMR Technical CampusDepartment of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha UniversityDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education FoundationDepartment of Electronics & Communication Engineering, Aditya UniversityAbstract Autonomous Aerial Vehicles (AAVs) play a significant role in emergency response and disaster management, such as during forest fires, earthquakes, and tsunamis. However, navigating through high-density obstacle environments poses substantial challenges due to process overheads, dynamic environmental conditions, and scalability issues. A novel Neighbour Awareness Strategy (NAS) protocol is proposed to address these challenges, focusing on efficient obstacle avoidance while maintaining safety, adaptability, and reactiveness. NAS protocol integrates Butterfly Magnetoreception Mechanism (BMM) and Machine Learning (ML) algorithms. BMM enhances navigational safety by reducing congestion and minimising decision-making delays during real-time events. ML algorithms ensure optimal energy consumption and risk mitigation by dynamically selecting the shortest path, even under abrupt environmental changes. Key navigation parameters, including orientation angle (θ), heading angle (α), and velocity vector (V), are used to achieve precise and adaptive control. Simulation results demonstrate that NAS outperforms existing methods, such as Adaptive Path planning with Dynamic Obstacle Avoidance (APA-DOA) and Real-time Environment Adaptive Trajectory Planning (REAT). It achieves superior obstacle avoidance, faster reaction times, and enhanced operational efficiency. Overall, the proposed NAS protocol enables AAVs to maintain optimal orientation during flight, adjust heading based on environmental inputs, coordinate swarm movements, and navigate complex three-dimensional spaces effectively.https://doi.org/10.1038/s41598-025-97283-xAutonomous aerial vehiclesGlobal path planningNeighbour awareness strategy (NAS)Data analyticsSpatial analysisButterfly magnetoreception mechanism |
| spellingShingle | Janjhyam Venkata Naga Ramesh C. Dastagiraiah Suraya Mubeen W. Deva Priya M. Kameswara Rao B. H. K. Bhagat Kumar Butterfly magnetoreception based neighbour awareness strategy protocol for autonomous aerial vehicles Scientific Reports Autonomous aerial vehicles Global path planning Neighbour awareness strategy (NAS) Data analytics Spatial analysis Butterfly magnetoreception mechanism |
| title | Butterfly magnetoreception based neighbour awareness strategy protocol for autonomous aerial vehicles |
| title_full | Butterfly magnetoreception based neighbour awareness strategy protocol for autonomous aerial vehicles |
| title_fullStr | Butterfly magnetoreception based neighbour awareness strategy protocol for autonomous aerial vehicles |
| title_full_unstemmed | Butterfly magnetoreception based neighbour awareness strategy protocol for autonomous aerial vehicles |
| title_short | Butterfly magnetoreception based neighbour awareness strategy protocol for autonomous aerial vehicles |
| title_sort | butterfly magnetoreception based neighbour awareness strategy protocol for autonomous aerial vehicles |
| topic | Autonomous aerial vehicles Global path planning Neighbour awareness strategy (NAS) Data analytics Spatial analysis Butterfly magnetoreception mechanism |
| url | https://doi.org/10.1038/s41598-025-97283-x |
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