The Problem with Fixed Parameters
Overwatch Orchestrate runs drone relay handoffs on a set of configurable parameters — swap threshold, patrol speed, transit speed, relay overlap. Out of the box, these defaults work. A 20% swap threshold gives the standby drone enough time to reach the handoff point. A patrol speed of 5 m/s balances coverage quality against battery endurance. Reasonable numbers for a reasonable day.
But days are not reasonable. Wind at 6 m/s increases battery drain by roughly 30%. That 20% swap threshold that gave you comfortable margins on a calm morning now leaves the active drone fighting headwind on its return leg with 12% remaining. A fleet of three drones with one in maintenance rotation means your standby pool is exactly one deep — and a 20% threshold that assumes two standbys ready to go is spending battery you cannot afford to spend.
Fixed parameters optimise for average conditions. Operations do not happen in average conditions.
What Fleet Advisor Does
Fleet Advisor is a decision engine built into the Overwatch ground station that dynamically tunes Orchestrate parameters every 30 seconds based on live fleet telemetry. It is not a chatbot. It is not a dashboard with recommendations. It is a system that reads battery state, wind conditions, fleet depth, and detection activity, then writes updated parameters directly into the Orchestrate configuration.
The engine models the real physics of the Parrot ANAFI UKR — battery discharge curves, wind drag penalties, transit power consumption, charge times. It knows that a drone at 25% SOC in 7 m/s wind does not have the same endurance margin as a drone at 25% in still air. It adjusts accordingly, every 30 seconds, for every drone in the fleet.
The Decisions It Makes
Swap threshold (15–30%). When to trigger the relay handoff. On a calm day with three standbys charged, the engine pushes this down toward 15% — extracting maximum flight time from each battery cycle. In 6 m/s wind with one standby available, it pulls up to 25% or higher. The margin expands to match the risk. Higher wind means earlier swap. Thin fleet means more conservative thresholds.
Relay overlap (0–30%). How much of the previous drone's route the incoming drone re-covers after handoff. In normal conditions, overlap is zero — the new drone picks up at the exact handoff waypoint and continues forward. When the active drone has flagged a detection event near the handoff zone, the engine increases overlap so the incoming drone re-sweeps that segment. A person in the water does not stay in one place. Re-covering 200 metres of route costs 90 seconds of flight time but catches drift.
Transit speed (1.0–2.0x). How fast the standby drone flies from the launch point to the handoff waypoint. The default is 1.5x patrol speed — fast enough to close the gap quickly, slow enough to preserve battery for the patrol leg. In high wind, the engine reduces transit speed. Fighting a headwind at 2.0x burns battery at 1.4x the normal transit rate. The engine knows the heading to the handoff point, knows the wind vector, and adjusts transit speed to keep total mission energy within the battery budget.
Patrol speed (2–8 m/s). Cruise speed during the patrol leg. Slower speed extends endurance — a drone at 3 m/s in 5 m/s wind lasts longer than the same drone at 6 m/s in the same wind, because the power curve is not linear. The engine reduces patrol speed in sustained wind to stretch each sortie. It increases speed when fleet depth is high and coverage tempo matters more than individual endurance.
Dispatch priority. Which standby drone to send next. The engine selects the drone with the highest state of charge. If two standbys are at 98% and 85%, the 98% drone dispatches. If a drone has been sitting on standby for 40 minutes and its battery has cooled, the engine accounts for the temperature-adjusted capacity. Simple rule, but it requires knowing the actual SOC of every drone in the fleet in real time.
The Physics Model
Fleet Advisor does not guess. It models the ANAFI UKR's real performance envelope.
Wind drain multiplier. Battery drain increases by approximately 10% for every metre per second of wind above the 3 m/s baseline. A drone in 7 m/s wind burns battery 40% faster than in still air. This is the single largest variable in relay timing — a 25-minute calm-air sortie becomes an 18-minute sortie in moderate wind. The engine reads the wind estimate from the active drone's telemetry and applies the multiplier to every endurance calculation.
Battery profiles. The standard ANAFI UKR battery delivers roughly 25 minutes of flight. The extended XLR battery — 70 minutes of flight time, 5 hours to charge. The engine knows which battery type is installed on each drone and adjusts all timing accordingly. A fleet running XLR batteries has fundamentally different relay dynamics than the same fleet on standard cells.
Transit drain. Flying to the handoff point at 1.5x speed costs 1.4x the power per kilometre compared to patrol speed. At 2.0x, the penalty is closer to 1.8x. The engine calculates the transit distance, applies the speed-dependent drain rate, and subtracts the transit energy budget from the total available for patrol. The remaining energy determines how long the drone can stay on station before the next handoff.
The Safety Boundary
Every parameter adjustment passes through a Safety Gate before it reaches Orchestrate. The gate enforces hard limits that the engine cannot override.
Critical battery threshold is fixed. The minimum SOC required for a drone to return home is calculated from distance, wind, and altitude — and the engine cannot set the swap threshold below it. If the calculated return energy is 14%, the swap threshold will never go below 15% regardless of what the optimisation logic suggests.
Geofence boundaries are immutable. No parameter adjustment can cause a drone to exit the defined operational area. Transit routes to handoff points are checked against the geofence before dispatch.
Wind critical is a hard gate. When sustained wind exceeds 8 m/s, the Safety Gate forces conservative parameters across the board — high swap threshold, reduced transit speed, reduced patrol speed. The engine's optimisation is overridden in favour of maximum safety margin.
Every decision the engine makes is logged with a full audit trail — the input telemetry, the calculated parameters, the Safety Gate result, and the final values written to Orchestrate. The operator can review exactly why every parameter changed and when.
Offline-First
Fleet Advisor runs entirely on the operator's laptop. The decision engine, the physics model, the Safety Gate — all local computation. Zero internet required. The system works identically on a clifftop with no cellular coverage as it does in a connected operations centre.
When connectivity exists — Starlink, cellular, field Wi-Fi — the engine can optionally hand decisions to Claude AI running in the cloud. The AI layer adds contextual reasoning that the local physics model cannot provide: weather forecast integration (wind increasing in 2 hours, front the swap threshold now), mission-specific pattern recognition, and natural language explanations of parameter changes for the operator log.
The UI shows the current decision source clearly: LOCAL or CLOUD. The operator always knows whether parameters are being set by the onboard physics engine or by the cloud-assisted AI. If connectivity drops mid-mission, the system falls back to LOCAL seamlessly. No degradation, no interruption, no operator action required.
What It Does Not Do
Fleet Advisor does not control drones mid-flight. A drone executing a patrol mission has its full flight plan baked into the mission package before launch. Waypoints, altitude, speed, camera angle — all set at dispatch time. The drone follows that plan autonomously from takeoff to landing. Nothing changes mid-flight.
The Advisor influences what gets loaded onto the next drone at dispatch time. In a relay operation, that decision point comes every 45 to 70 minutes — each time a new standby drone is prepared for launch. The engine calculates the optimal parameters for the next sortie based on current conditions, writes them into the mission package, and Orchestrate dispatches the drone with those parameters baked in.
This is a deliberate design constraint. A drone in flight is a closed system executing a validated plan. Modifying parameters mid-flight introduces failure modes — communication latency, partial updates, conflicting commands. Fleet Advisor operates at the relay boundary, not the flight boundary. It makes each sortie smarter without adding complexity to the flight itself.
Try It
Fleet Advisor ships as part of Overwatch Orchestrate. If you are running Orchestrate today, the Advisor is already in your ground station software — enable it in the fleet configuration panel. If you are evaluating Overwatch for continuous patrol operations, request a demo and we will walk through the Advisor's decision logic on a live fleet.