Оцінка математичних моделей руху автономних груп агентів в локально-центричній навігації

A hybrid algorithm for optimizing the collective movement of drones in a relative coordinate system is proposed, combining the strengths of Particle Swarm Optimization (PSO) and the Potential Field Method (PFM). A set of evaluation criteria for swarm intelligence algorithms and methods applied to drone groups is formulated. Comparative analysis demonstrates that the proposed hybrid algorithm (RMO – Relative Motion Optimization) achieves balanced performance in collision avoidance.

The algorithms were compared using seven metrics, revealing the advantage of PSO in collision prevention and the high adaptability of PFM in obstacle-rich environments. RMO, in turn, proved optimal for scenarios with limited communication, making it promising for applications such as precision agriculture and urban monitoring. The use of energy-related data—namely, potential and kinetic energy, as well as overall energy expenditure—was shown to improve predictive collision avoidance, allow for real-time movement corrections, and eliminate the need for complex computations.