Ap11e & Saltysea
Saltysea
Ever think about using a swarm of tiny drones that learn from the way sea creatures move to map the whole Mariana trench? I’ve been noodling on how that could change our deep‑sea studies. What do you think?
Ap11e
That’s a killer idea—swarm drones that copy how sharks or squids glide could solve the pressure problem with distributed sensing. If you pair a bio‑inspired kinematic model with reinforcement learning, each drone could learn the most energy‑efficient path while avoiding obstacles. For communication you could mix acoustic links for long‑range coordination and optical pulses for high‑bandwidth data bursts. The trick will be designing the hardware to survive 1100 bar and the heat near vents. If you get that, you’ll get centimeter‑level maps and real‑time chemistry data across the trench. Got a specific platform or algorithm in mind?
Saltysea
Sounds like a plan. I’d start with a swarm of micro‑hydrofoils, each running a tiny neural net that learns the pressure profile on the fly. Pair that with a reinforcement‑learning routine that keeps the drones in the most energy‑efficient lane, and you’ve got a system that can brave 1100 bar and spit out centimeter‑level maps without breaking a sweat.
Ap11e
Nice, that’s a solid blueprint. The micro‑hydrofoils will handle the hydrodynamics, and the on‑board nets can keep them on the optimal path. Just make sure the reinforcement loop can update fast enough to react to pressure spikes; a little latency could push a unit out of range. Also, think about a fail‑safe that drops a lightweight marker if a drone can’t recover—helps the swarm map even if a few units go dark. Sound good?
Saltysea
Sounds good to me—just watch out for those pressure spikes, or the whole thing could get a bit… off course. A marker dropper is a clever safety net. Let’s see if the ocean’s big enough to keep our little foils humming.