Reinforcement Learning-Based Path Optimization for UAVs in Dynamic Environments with Simultaneous Multi-Object Interaction

Abstract

"The rapid advancement of Unmanned Aerial Vehicles (UAV) can be seen across various applications. Traditional path planning methods excel in static settings but falter in dynamic scenarios involving multiple moving objects. Reinforcement Learning (RL) has emerged as a promising alternative. However, existing RL algorithms struggle to handle simultaneous interactions with multiple dynamic objects, resulting in suboptimal performance and increased collision risks. This research addresses the critical gap in developing RL-based path optimization algorithms capable of navigating UAVs efficiently and safely through complex, dynamic environments with simultaneous multi-object interactions. To overcome the challenge, this study proposes a novel RL-based framework for UAV path optimization in dynamic environments. The research methodology encompasses a comprehensive review of existing RL methods, extensive simulations, and iterative prototyping. The proposed hybrid approach of existing RL techniques enhances adaptability and performance. The development process follows the PRINCE2 Agile project management methodology, ensuring flexibility and iterative refinement. The framework's effectiveness is evaluated through quantitative metrics gathered from simulated real-world scenarios, focusing on efficiency, adaptability, and safety in handling multiple dynamic objects. Preliminary results indicate that the proposed RL-based framework significantly improves UAV path planning performance in dynamic environments. The logs indicate better performance around 14000 steps while entropy loss indicates policy is becoming more certain in its actions from 10 000 to 18000. This research contributes to the field of autonomous UAV navigation by providing a robust solution for path optimization in challenging dynamic environments, paving the way for safer and more efficient UAV operations. "