Toward Autonomous UAV Swarm Navigation: A Review of Trajectory Design Paradigms

Area of Science:

• Robotics and Autonomous Systems
• Artificial Intelligence
• Control Theory

Background:

• Unmanned aerial vehicle (UAV) swarms are crucial for diverse applications like surveillance and agriculture.
• Efficient trajectory planning is vital for swarm coordination, safety, and mission success.
• Existing methods face challenges in scalability, energy efficiency, and real-time adaptation.

Purpose of the Study:

• To provide a comprehensive review of UAV swarm trajectory planning techniques.
• To critically compare traditional, bio-inspired, and AI-based methods.
• To identify challenges and future research directions in autonomous swarm operations.

Main Methods:

• Categorization of trajectory planning techniques into traditional algorithms, bio-inspired metaheuristics, and AI-based methods.
• Comparative analysis of algorithms based on computational efficiency, scalability, coordination, energy consumption, and robustness.
• Examination of hybrid frameworks combining bio-inspired and AI approaches.

Main Results:

• Traditional algorithms offer baseline solutions, while bio-inspired methods excel in global optimization.
• AI-based methods provide real-time adaptability and adaptive decision-making.
• Hybrid frameworks balance exploration-exploitation for enhanced multi-agent performance.

Conclusions:

• UAV swarm trajectory planning requires balancing centralized and decentralized control for optimal performance.
• Addressing challenges like multidimensional spaces and nonlinear dynamics is key for future advancements.
• This review serves as a resource for developing intelligent, autonomous UAV swarm missions.