Quantum-enhanced artificial potential field method for robust drone swarm shape formation

Distributed coordination of a swarm of drones is one of the inherent open problems in autonomous aerial robotics, as classical approaches suffer from slow convergence and poor resilience to disturbances. In this paper, an efficient and robust approach to shape formation of drone swarms is offered based on Quantum-Enhanced Artificial Potential Field (QEAPF). This method combines quantum-inspired probabilistic discovery mechanisms with Artificial Potential Field (APF) techniques. By incorporating adaptive parameter tuning, explicit disturbance estimation and compensation, and quantum-inspired probabilistic exploration. QEAPF significantly demonstrates improvements in formation convergence time, path efficiency, and disturbance rejection capabilities. Thorough simulation-based evaluations of the QEAPF method produce up to a 37% improvement in formation convergence time and a 42% improvement in disturbance rejection performance compared to traditional APF techniques. QEAPF has been shown to smoothly organize itself into a target configuration while maintaining collision avoidance, energy efficiency, and geometric integrity.