Resilient cloud-assisted coordination for multi-UAV low-altitude economy services: Battery-aware coverage reallocation for agricultural sensing

The low-altitude economy (LAE) is driving rapid growth in autonomous multi-UAV services that must operate over wide, infrastructure-poor areas under intermittent connectivity and strict energy budgets. For such services, the central challenge is not only sensing but resilient networking and coordination : distributing missions, aggregating telemetry, and recovering coverage when UAVs fail or run out of battery. This paper presents a resilient, cloud-assisted ad hoc coordination architecture for multi-UAV LAE services, using precision agriculture as a demanding, representative use case. Missions, monitoring and recovery operate on compact survey-line units rather than waypoints, which keeps coordination and recovery messages at hundreds of bytes and separates them from imagery. On this substrate we propose a battery-aware reallocation mechanism with a look-ahead trigger and a feasibility test based on one empirically calibrated energy model. We also propose a decentralized recovery protocol in which the shared line plan provides failure detection, bounds each recovery to a small cohort of UAVs and makes the reassignment a deterministic function of a frozen record, so that survivors agree without a ground station or auction rounds. The operational workflow was field-validated with DJI Mavic 3M UAVs on agricultural sites in Spain. Survey kinematics were measured, and an energy model was fitted on ten flight logs and cross-validated against measured battery consumption (energy error within ± 3.3 %). Coordination traffic was network-emulated, and all failure-recovery experiments were software-simulated with a single co-simulator using the calibrated parameters. In simulation, recovery kept every landing above the reserve, even with a ＋30% energy-model bias. In the six-UAV vineyard experiments, recovery required approximately 1–3 KB of signalling per event. The share of orphaned work recovered within the sortie was governed by the fleet load factor: complete for lightly loaded fleets, but only 6–8 percentage points for a six-UAV vineyard mission sized to the battery limit. The decentralized protocol matched centralized recovery without a ground station, stayed consistent under cascading failures, 50% loss and reordering, and scaled to 48 UAVs at under 80 B/s per node.