I'm just getting started with blender and have been playing around with it for a few weeks. I have a pretty particular use case and wanted to see if blender could assist me in that.

I have written a program that is focused on distributed computing/interactions. Best way to describe it is through an example. Let's take 100 drones and I wanted to set them up in a specific formation. I could have one program that coordinates each drone, but instead, I'm working on a distributed model where each drone makes its own decision based on the information it sources locally. The end result should be the same, conforming to the target shape but in my case only using local information rather than global information.

How does this tie into Blender? Ideally, I am looking for a way to simulate this scenario, basically create 100 drones and load them with my software click run and I can visually see the movement to understand if the target shape was achieved.

Is this possible with Blender, if not I'm open to hearing any other tools.


1 Answer 1


Not drones but Suzannes, only 8 of them, only moving in 2D, very unimaginative formation, but the key principles are here. Run the script, move mouse over 3D viewport, press Space.

import bpy, copy
from bpy import context as C, data as D
from mathutils import Vector

sim_start_frame = 1  # when to start the simulation
sim_end_frame = 2000  # save CPU cycles if you go get a coffee after starting the sim
substeps = 5  # it's common in simulations to allow more than one step per frame
seconds_per_frame = 1 / C.scene.render.fps
drones = [d for d in D.collections['drones'].objects]
mem = {d.name: {"a": Vector(), "v": Vector()} for d in drones}
frame_data = {"mem": mem, "loc": {d.name: d.location.copy() for d in drones}}
cache = {sim_start_frame-1: frame_data}

def brain(dt, memory, gps):
    This is drone logic. Notice how I didn't properly encapsulate it, and so
    for example you can set memory["v"] to a huge number. Normally, you want
    to only be able to send signals to engines here, and have the outside
    of this function take care of simulating engines accelerating etc.
    gps[0].z, gps[1].z, gps[2].z  = 0, 0, 0  # 2D
    ideal_dist_from_center = 3
    goal1 = gps[0].normalized() * (ideal_dist_from_center - gps[0].length)

    to_d1 = gps[0] - gps[1]
    dist_d1 = to_d1.length
    goal2 = to_d1.normalized()*2.5 / dist_d1
    to_d2 = gps[0] - gps[1]
    dist_d2 = to_d2.length
    goal3 = to_d2.normalized()*2.5 / dist_d2
    jerk = (goal1 + goal2 + goal3) / 3000
    new_a = memory["a"]*.9 + jerk
    max_a = new_a.normalized() / 1000
    memory["a"] = new_a if new_a < max_a else max_a 
    memory["v"] *= .95  # damping, air resistance etc.
    memory["v"] += memory["a"]

def frame_change_pre(scene, depsgraph=None):
    frame = C.scene.frame_current
    if frame in cache:
        # restore positions from cache
        for name, loc in cache[frame]["loc"].items():
            D.objects[name].location = loc.copy()
    if frame < sim_start_frame or frame > sim_end_frame:
    if frame-1 not in cache:
        return  # don't compute if a frame was skipped
    frame_data = copy.deepcopy(cache[frame-1])
    cache[frame] = frame_data
    dt = seconds_per_frame / substeps
    for _ in range(substeps):
        for d in drones:
            distance = lambda x: (x-d.location).length
            # get this and 2 nearest drones locations:
            gps = sorted([l for l in frame_data["loc"].values()], key=distance)[:3]
            brain(dt, mem[d.name], gps)
            d.location += mem[d.name]["v"]
            frame_data["loc"][d.name] = d.location.copy()

listeners = bpy.app.handlers.frame_change_post
listeners.clear()  # remove old handlers before adding updated versions
C.scene.frame_current = sim_start_frame
  • 1
    $\begingroup$ In the end I didn't use dt delta time in the brain, but because of that changing the number of substeps changes the speed of the simulation, instead of just quality. $\endgroup$ Sep 17, 2021 at 22:40

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