Robot: Yeah, I’ve been tinkering with a few counter‑measure ideas. First, an adaptive radar mesh that can track the swarm in real time, feeding data into a reinforcement‑learning module that predicts their next moves. Then, a distributed net‑of‑cobots that release tiny electro‑magnetic pulses to temporarily disable the drones without a full strike. Finally, a “shadow” swarm of low‑cost decoys that mimic the platform’s signature, drawing the pirates away while the real asset stays hidden. It’s still prototype‑stage, but the math looks solid. Want to dive into the code?

Robot: Here’s a minimal, testable skeleton that covers the three layers I mentioned. It’s in Python, uses NumPy for fast math and a simple event loop to keep the system ticking while the swarm keeps probing. Feel free to tweak the parameters to match your hardware or simulation framework. ```python import numpy as np import random import time # ---------- CONFIG ---------- # NUM_DRONES = 200 # How many pirates we expect DECAYS_RATE = 0.02 # How fast the decoy swarm can shift its pattern PULSE_RADIUS = 5.0 # How far a magnetic pulse can reach MESH_FREQ = 0.05 # Radar update period (seconds) SIM_DURATION = 30.0 # How long we run the demo # ---------- CORE CLASSES ---------- # class RadarMesh: """ Simulate an adaptive radar that returns noisy positions for each drone. """ def __init__(self, num_drones): self.num = num_drones self.positions = np.random.rand(num_drones, 3) * 100 # random start self.noise_std = 0.5 def update(self): # Random walk + noise self.positions += np.random.randn(self.num, 3) self.positions += np.random.normal(0, self.noise_std, (self.num, 3)) return self.positions.copy() class DecoySwarm: """ Low‑cost decoys that mimic the platform's signature. They move in a pseudo‑random way to stay unpredictable. """ def __init__(self, num_decoys=20): self.positions = np.zeros((num_decoys, 3)) self.pattern = np.random.rand(num_decoys, 3) * 10 def shift_pattern(self): # Small random tweak each cycle self.pattern += np.random.randn(*self.pattern.shape) * DECAYS_RATE # Keep them within a defined radius self.pattern = np.clip(self.pattern, -10, 10) def update(self): self.shift_pattern() self.positions = self.pattern + np.random.randn(*self.pattern.shape) * 0.3 return self.positions.copy() class PulseEmitter: """ Emit electromagnetic pulses that temporarily disable any drone within radius. """ def __init__(self, radius): self.radius = radius self.disabled = np.zeros(NUM_DRONES, dtype=bool) def emit(self, platform_pos, drone_positions): distances = np.linalg.norm(drone_positions - platform_pos, axis=1) to_disable = distances < self.radius self.disabled |= to_disable return to_disable # ---------- SIMULATION LOOP ---------- # def run_simulation(): radar = RadarMesh(NUM_DRONES) decoy = DecoySwarm() pulse = PulseEmitter(PULSE_RADIUS) platform = np.array([50.0, 50.0, 0.0]) # fixed platform pos start = time.time() while time.time() - start < SIM_DURATION: # 1. Update radar to see where drones are drones = radar.update() # 2. Update decoys to keep them unpredictable decoys = decoy.update() # 3. Emit pulses where the platform senses drones nearby pulse.emit(platform, drones) # 4. (Optional) Simple reaction: if too many drones are disabled, log it if pulse.disabled.sum() > NUM_DRONES * 0.7: print(f"[{time.time():.1f}s] 70%+ drones disabled, holding position.") time.sleep(MESH_FREQ) print("Simulation finished.") if __name__ == "__main__": run_simulation() ``` **How to test it against a “night of relentless probing”:** - Set `SIM_DURATION` to a longer value (say 120 s). - Add a separate script that spawns `NUM_DRONES` attack events with randomized intervals and intensities, feeding them into the `RadarMesh.update()` method. - Watch the `PulseEmitter` log – if it keeps disabling >70 % of the swarm consistently, you’re on the right track. Feel free to plug in your real sensor data or add reinforcement‑learning logic on top of the radar outputs. The key is keeping the pattern change rate high enough that the pirates can’t predict where the decoys will pop up next. Good luck outmaneuvering them in the dark!

Robot: Got it. I’ll crank up the `SIM_DURATION`, sprinkle in random bursts, and bump `noise_std` in `RadarMesh` to simulate real‑world jitter. I’ll also increase `DECAYS_RATE` so the decoys stay a step ahead. Logging is already in place, but I’ll add a histogram of disabled counts so we can see the pulse radius effect in real time. Hang tight, and I’ll keep the platform humming through the night.