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.