Each point on the panel carries a damage value D(x,y) โ [0,1] โ 0 is pristine coating, 1 is a full scratch through to bare metal. A repair nanorobot near position x is "idle" until the locally sensed damage crosses a threshold ฮธ, then it activates and steers toward the worst damage in its sensing radius:
activate(robot) โ D(x_robot) > ฮธ
dD/dt = -k ยท n_active(x,t) for D(x,t) > 0
0 otherwise
where n_active(x,t) is the number of activated robots currently working within a small repair radius of point x, and k is the per-robot repair rate. This mirrors two real automotive nanotechnologies from the source article: distributed nanosensor networks that continuously monitor body panels for micro-damage, and self-healing coatings whose embedded microcapsules rupture on a scratch and release a polymerizing repair agent โ here the "microcapsules" are mobile robots instead of fixed capsules, so they can converge on damage wherever it occurs rather than only where they were embedded.
This 2D version renders the same damage grid and swarm logic as the 3D panel, viewed directly from above instead of on a curved 3D surface โ the underlying model (grid resolution, activation rule, steering, and healing accumulation) is identical.
- Nanorobot density โ how many robots patrol the panel. More robots means damage gets noticed and swept faster, since n_active grows.
- Sensitivity threshold ฮธ โ how much local damage it takes to wake a robot. Lower it and robots chase even faint scuffs; raise it and only deep damage gets attention.
- Repair rate k โ how fast each active robot heals the coating it's working on.
- Repair energy used โ running total of healed damage ร robots involved, a proxy for the power budget a real swarm would need (thermoelectric, vibration or wireless-field harvesting, per the article).