Scaling Autonomous Flight: Coordinating Mechanics, Edge?AI, and Policy to Surpass Energy and Swarm Limits

Small autonomous drones have moved from prototypes to operational assets, yet research and deployment remain fragmented across mechanical, computational, and regulatory silos. This paper delivers a systems?level synthesis that unites these strands and quantifies their interaction on performance. We apply a three?phase methodology: (i) comparative case studies of mature deployments in medical logistics, ecological restoration, and public entertainment; (ii) parametric trade?off modelling that links air?frame mass, battery energy density, compute load, and mesh latency to range and payload; and (iii) causal?loop analysis. Six findings emerge: 1) order?of?magnitude gains in speed and coverage; 2) a 300?Wh?kg?? energy ceiling that restricts long?range BVLOS missions; 3) compute mass and packet loss jointly capping swarms at ?300 units; 4) a feedback loop tying power, connectivity, and cyber?security; 5) fragmented BVLOS and Remote?ID rules inflating costs and delays; and 6) community acceptance driven by demonstrated benefit and sub?45?dB acoustic profiles. Empirical data spanning over a million sorties and thousands of synchronized flight?hours ground the analysis. Isolated optimisation simply migrates bottlenecks; only coordinated advances across energy, edge intelligence, networking, and policy can unlock the next performance tier. The paper closes with a staged roadmap to scale autonomous flight into resilient, equitable infrastructure.