Analysis and Implementation of Motion Planning Algorithms for Real-Time Navigation of Aerial Robots in Dynamic Environments

Efficient path planning is critical for the safe and effective navigation of Autonomous Aerial Robots (AARs) in complex 3D environments. This paper presents a comprehensive exploration of path planning techniques designed for AAR operations, with a focus on the application of the D* algorithm and its variants. Beginning with an overview of traditional methods such as Dijkstra's and A* algorithms, this work delves into the advanced capabilities of the D* algorithm family, including dynamic replanning and adaptive optimization for changing environmental conditions. A key contribution of this work is the implementation of these algorithms in a manner that significantly accelerates the search phase, particularly when dealing with multiple robots and goals, enhancing computational efficiency. Through detailed analysis and simulation, this study investigates the practical implementation of these algorithms in 3D space, addressing challenges such as obstacle avoidance, computational efficiency, dynamic feasibility, and real-time adaptability. Our simulated case results demonstrate the efficacy and versatility of these techniques in various AAR missions, providing valuable insights for researchers and practitioners in the field of autonomous aerial navigation. This work not only highlights the strengths and limitations of existing methods but also offers novel solutions to improve the performance of path planning algorithms in dynamic environments.