Skip to content
main
Switch branches/tags
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 
 
 

README.md

Swarms

ENGR3590: A Computational Introduction to Robotics, Olin College of Engineering, FA2020

Nathan Estill, Jonathan Zerez


In this project, we set out to create a simulated decentralized robot swarm that would exhibit self-assembling properties. Specifically, we wanted to replicate morphogenesis, the process by which cells are able to self-organize themselves in order to form complex tissues and organs through local interactions alone. This is of great interest to us, as decentralized systems are full of complexity, and are quite different from centralized robotic systems. Decentralized swarms offer a number of really cool advantages in that they are very robust to external noise, damage to a number of individual agents within the swarm, and unpredictable variations in the environment.

If you would like to read more, check out the website

For reference, we have also created an important demo video for the project, which can be found here.

To Run

python3 Simulator.py

If you want to change parameters yourself, change the parameters at the bottom of Simulator.py. The parameters are, in order:

  • nSteps: the number of time steps the simulation will run for
  • gridSize: the overall size of the space the robots will be in
  • rdParams: a tuple, with the following:
    • cA: the diffusion ratio of A
    • cB: the diffusion ratio of B
    • a_add_rate: the amount of A to add at each step
    • b_add_rate add rate: the amount of B to add at each step (usually negative to remove B)
  • sideLength: the length of the side of the square of robots, or diameter if a circle
  • stepsPerFrame: the number of time steps between each captured frame of the animation
  • stepsPerChemicalUpdate: the number of time steps between when the robots update their chemical concentration
  • stepsPerRobotMovement: the number of time steps between when the robots move

As a potential starting point for the rdParams, the values (0.43,0.19,0.035,-0.1) will give polka-dots, and the values (0.5,0.25,0.06,-0.124) will give a worm.

Dependencies

  • numpy
  • matplotlib
  • scipy

About

How does complexity arise from relatively simple agents?

Resources

Releases

No releases published

Packages

No packages published

Languages