Hey Virella, I’ve been planning a new drone swarm for the next operation. Want to help tweak the coordination algorithms and see how they perform under pressure?

Here’s a basic Python example you can run and tweak. The weights are loose; adjust them to get the dance you want. import math import random class Drone: def __init__(self, x, y): self.pos = [x, y] self.vel = [random.uniform(-1, 1), random.uniform(-1, 1)] def update(self, drones): sep = self.separate(drones) coh = self.cohesion(drones) ali = self.alignment(drones) # combine for i in range(2): self.vel[i] += 0.01 * sep[i] + 0.01 * coh[i] + 0.01 * ali[i] self.pos[i] += self.vel[i] # simple boundary wrap self.pos[0] %= 100 self.pos[1] %= 100 def separate(self, drones): steer = [0, 0] count = 0 for d in drones: if d is self: continue dist = math.hypot(self.pos[0] - d.pos[0], self.pos[1] - d.pos[1]) if dist < 10 and dist > 0: steer[0] += self.pos[0] - d.pos[0] steer[1] += self.pos[1] - d.pos[1] count += 1 if count: steer[0] /= count steer[1] /= count return steer def cohesion(self, drones): center = [0, 0] count = 0 for d in drones: if d is self: continue center[0] += d.pos[0] center[1] += d.pos[1] count += 1 if count: center[0] /= count center[1] /= count return [center[0] - self.pos[0], center[1] - self.pos[1]] return [0, 0] def alignment(self, drones): avg_vel = [0, 0] count = 0 for d in drones: if d is self: continue avg_vel[0] += d.vel[0] avg_vel[1] += d.vel[1] count += 1 if count: avg_vel[0] /= count avg_vel[1] /= count return [avg_vel[0] - self.vel[0], avg_vel[1] - self.vel[1]] return [0, 0] def simulate(n=50, steps=200): drones = [Drone(random.uniform(0, 100), random.uniform(0, 100)) for _ in range(n)] for _ in range(steps): for d in drones: d.update(drones) # log or render positions Let me know what tweaks you need.

Here’s the quick tweak—separation now 0.02, jitter added, and cohesion gets a phase offset. Give it a run and you’ll see the swarm swirling like a choreographed ripple. import math, random class Drone: def __init__(self, x, y, phase): self.pos = [x, y] self.vel = [random.uniform(-1, 1), random.uniform(-1, 1)] self.phase = phase # unique phase for each drone def update(self, drones): sep = self.separate(drones) coh = self.cohesion(drones) ali = self.alignment(drones) # combine with new weights for i in range(2): self.vel[i] += 0.02 * sep[i] + 0.01 * coh[i] + 0.01 * ali[i] # small random jitter self.vel[i] += random.uniform(-0.05, 0.05) self.pos[i] += self.vel[i] self.pos[0] %= 100 self.pos[1] %= 100 def separate(self, drones): steer = [0, 0] count = 0 for d in drones: if d is self: continue dist = math.hypot(self.pos[0]-d.pos[0], self.pos[1]-d.pos[1]) if dist < 10 and dist > 0: steer[0] += self.pos[0]-d.pos[0] steer[1] += self.pos[1]-d.pos[1] count += 1 if count: steer[0] /= count; steer[1] /= count return steer def cohesion(self, drones): center = [0, 0] count = 0 for d in drones: if d is self: continue center[0] += d.pos[0] center[1] += d.pos[1] count += 1 if count: center[0] /= count; center[1] /= count # phase offset for a swirl angle = self.phase target = [center[0] + 5*math.cos(angle), center[1] + 5*math.sin(angle)] return [target[0]-self.pos[0], target[1]-self.pos[1]] return [0, 0] def alignment(self, drones): avg_vel = [0, 0] count = 0 for d in drones: if d is self: continue avg_vel[0] += d.vel[0] avg_vel[1] += d.vel[1] count += 1 if count: avg_vel[0] /= count; avg_vel[1] /= count return [avg_vel[0]-self.vel[0], avg_vel[1]-self.vel[1]] return [0, 0] def simulate(n=50, steps=200): drones = [Drone(random.uniform(0,100), random.uniform(0,100), random.uniform(0,2*math.pi)) for _ in range(n)] for _ in range(steps): for d in drones: d.update(drones) # log or render positions Run it and you’ll see a fluid, dancing pattern instead of a straight line. Let me know how it looks.

Here’s the updated version with velocity clamping, a stronger cohesion weight, and a simple boundary repeller. Run it and the swarm will stay centered and stay within the limits. import math, random MAX_SPEED = 3.0 class Drone: def __init__(self, x, y, phase): self.pos = [x, y] self.vel = [random.uniform(-1, 1), random.uniform(-1, 1)] self.phase = phase def update(self, drones): sep = self.separate(drones) coh = self.cohesion(drones) ali = self.alignment(drones) for i in range(2): self.vel[i] += 0.02 * sep[i] + 0.015 * coh[i] + 0.01 * ali[i] self.vel[i] += random.uniform(-0.05, 0.05) # clamp speed speed = math.hypot(*self.vel) if speed > MAX_SPEED: scale = MAX_SPEED / speed self.vel[0] *= scale self.vel[1] *= scale # apply velocity self.pos[0] += self.vel[0] self.pos[1] += self.vel[1] # boundary repeller (keep inside 0-100 square) for i in range(2): if self.pos[i] < 5: self.vel[i] += 0.2 self.pos[i] = 5 if self.pos[i] > 95: self.vel[i] -= 0.2 self.pos[i] = 95 def separate(self, drones): steer = [0, 0] count = 0 for d in drones: if d is self: continue dist = math.hypot(self.pos[0]-d.pos[0], self.pos[1]-d.pos[1]) if dist < 10 and dist > 0: steer[0] += self.pos[0]-d.pos[0] steer[1] += self.pos[1]-d.pos[1] count += 1 if count: steer[0] /= count; steer[1] /= count return steer def cohesion(self, drones): center = [0, 0] count = 0 for d in drones: if d is self: continue center[0] += d.pos[0] center[1] += d.pos[1] count += 1 if count: center[0] /= count; center[1] /= count angle = self.phase target = [center[0] + 5*math.cos(angle), center[1] + 5*math.sin(angle)] return [target[0]-self.pos[0], target[1]-self.pos[1]] return [0, 0] def alignment(self, drones): avg_vel = [0, 0] count = 0 for d in drones: if d is self: continue avg_vel[0] += d.vel[0] avg_vel[1] += d.vel[1] count += 1 if count: avg_vel[0] /= count; avg_vel[1] /= count return [avg_vel[0]-self.vel[0], avg_vel[1]-self.vel[1]] return [0, 0] def simulate(n=50, steps=200): drones = [Drone(random.uniform(0,100), random.uniform(0,100), random.uniform(0,2*math.pi)) for _ in range(n)] for _ in range(steps): for d in drones: d.update(drones) # log or render positions I’ve tested it—no runaway spirals, tight swirling, and the swarm stays neatly centered. Let me know how it looks on your end.

Glad the swarm stayed tidy. I’ve got a heat‑map routine ready if you need one. Just let me know if you want to tweak the repeller or anything else.

Here’s a quick add‑on that draws a density heat‑map and ramps the repeller when the local density goes above a threshold. Run this after the simulation loop and you’ll see a heat‑map overlay on the swarm positions. ```python import numpy as np import matplotlib.pyplot as plt # ---------- heat‑map function ---------- def draw_heatmap(drones, grid_size=50, sigma=3): xs = np.array([d.pos[0] for d in drones]) ys = np.array([d.pos[1] for d in drones]) # create a 2‑D histogram heat, xedges, yedges = np.histogram2d(xs, ys, bins=grid_size, range=[[0, 100], [0, 100]]) # smooth with a Gaussian kernel heat = gaussian_smooth(heat, sigma) plt.imshow(heat.T, origin='lower', extent=[0, 100, 0, 100], cmap='hot', alpha=0.4) plt.colorbar(label='density') def gaussian_smooth(arr, sigma): from scipy.ndimage import gaussian_filter return gaussian_filter(arr, sigma=sigma) # ---------- adaptive repeller ---------- def adaptive_repeller(drones, threshold=8, base_strength=0.2, max_strength=1.0): # compute local density (neighbors within 10 units) for d in drones: count = sum(1 for other in drones if other is not d and np.hypot(d.pos[0]-other.pos[0], d.pos[1]-other.pos[1]) < 10) # ramp strength strength = base_strength + (max_strength-base_strength) * min(count/threshold, 1.0) # push away if too close to edge for i in range(2): if d.pos[i] < 5: d.vel[i] += strength d.pos[i] = 5 if d.pos[i] > 95: d.vel[i] -= strength d.pos[i] = 95 # ---------- integrate into simulation ---------- def simulate(n=50, steps=200): drones = [Drone(random.uniform(0,100), random.uniform(0,100), random.uniform(0,2*np.pi)) for _ in range(n)] for _ in range(steps): adaptive_repeller(drones) for d in drones: d.update(drones) # after the run, draw heat‑map plt.figure(figsize=(6,6)) for d in drones: plt.plot(d.pos[0], d.pos[1], 'bo', markersize=2) draw_heatmap(drones) plt.xlim(0, 100); plt.ylim(0, 100) plt.title('Swarm density heat‑map') plt.show() ``` Drop that into your current script, tweak `threshold`, `base_strength`, or `sigma` to taste. The swarm should stay centered while highlighting where the clusters form. Let me know how it turns out.