Gridkid & Drax
Gridkid Gridkid
Drax, I’ve been working on a modular drone that can swap out its sensor array mid‑flight—think of it as a chameleon in the sky. I’d love to see your take on the optimal flight plan, while I push its adaptability against random obstacles.
Drax Drax
Plan: start at a high altitude to see the field, then descend in a straight line while scanning. Divide the mission into three phases. Phase one: fly a grid with a fixed speed and note obstacle density. Phase two: when you hit a high‑density zone, trigger a sensor swap—swap to a long‑range radar or lidar to map the obstacles precisely. Phase three: return to base along the same grid but with the new sensor data, avoiding any identified hazards. Keep the swap timing tied to a waypoint so the drone has a few seconds to re‑calibrate before it continues. Keep the flight path predictable, avoid improvisation, and you’ll have a clean, efficient run.
Gridkid Gridkid
Sounds solid, but why stop at just one swap? If the obstacle density keeps spiking, the drone could keep swapping sensors on the fly—maybe even add a thermal or hyperspectral module for a backup view. And keep the waypoints a bit fuzzy; a little buffer in the path gives the system breathing room if something unexpected pops up. Still, your phased approach is a great baseline to iterate on.
Drax Drax
You can keep swapping, but each swap costs time. Set a maximum of three transitions per mission and a buffer of five seconds for re‑calibration. Use a small margin on the waypoints—two meters instead of one—to avoid over‑reacting to jitter. The plan stays tidy if you limit the swap count and keep the path structured.
Gridkid Gridkid
Got it—so it’s three swaps max, five seconds each, and a two‑meter margin. I’ll tweak the waypoint algorithm to keep the drone a bit farther from the grid lines, so we’re not flipping switches on every tiny bump. That should keep the mission tight and the timing predictable. Let’s run a quick simulation and see how the buffer plays out.