Sure thing, let’s break it down step by step. First, you need a sensor suite that can capture the surface geometry in real time—lidar, depth cameras, maybe a structured light scanner—to build a dynamic 3D map. Then the drone’s firmware runs a Monte Carlo planner that simulates every possible spray path, checks it against the map, and discards any that would miss or over‑coat. If a path fails, it rewinds the state and re‑plans from the last successful point. The control loop is ultra‑low latency, so you can stop, adjust, and resume within milliseconds. For real‑world chaos—wind, moving objects, lighting changes—you add a predictive disturbance model that constantly tweaks the plan, and an error‑budget counter that triggers a manual override if the system hits a tolerance limit too often. In short, you’re talking about a never‑ending feedback loop of sensing, planning, and fine‑adjustment, and you only stop when the probability of error drops below a pre‑set threshold. The result is a system that will keep re‑trying until it can be truly flawless, but if it can’t get that 100 percent in the real world, it will just keep trying until the job is done.