ANALYSIS OF METHODS AND ALGORITHMS FOR TRAJECTORY PLANNING IN MULTI-UAV APPLICATIONS

Abstract

The paper provides a comprehensive analysis of modern methods and algorithms for trajectory planning in multi-UAV (Unmanned Aerial Vehicle) systems, emphasizing their relevance in both military and civilian applications. Traditional approaches, including graph-based methods such as A* and sampling-based methods like Rapidly-exploring Random Tree (RRT), are examined with regard to their computational efficiency, adaptability, and limitations in dynamic environments. The study highlights the increasing importance of intelligent techniques, particularly evolutionary algorithms such as Genetic Algorithms (GA) and Differential Evolution (DE), as well as bio-inspired methods like Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). These algorithms are evaluated in terms of their ability to address challenges related to real-time planning, multi-agent coordination, scalability, and resilience to dynamic changes.

The article identifies key factors that critically affect UAV trajectory planning: mission type, obstacle complexity, communication reliability, energy and computational constraints, and the ability to replan under uncertain conditions. A comparative analysis is presented, summarizing the advantages, drawbacks, and application domains of each algorithm. The results show that classical methods remain efficient in static or partially known environments, whereas intelligent approaches demonstrate greater flexibility and global optimization capabilities in complex, dynamic scenarios. However, their high computational demands may limit applicability in real-time missions.

The study concludes that the choice of algorithm must be guided by the nature of the mission, environmental complexity, and available resources. Future research directions include the development of hybrid solutions combining classical and intelligent methods, adaptive algorithms capable of real-time decision-making under uncertainty, and resilient architectures for large-scale UAV swarms. Such advancements will enhance autonomy, reliability, and efficiency of UAV applications in both defense and civilian sectors.