Genetic Algorithm Evolving Robot
We calculate the final price from your selected file. You review and accept it before payment.
Saved on this device.
Model information
- Source
- Printables
- Category
- Electronics
- License
- Creative Commons — Attribution — Noncommercial
Full medium article can be found here:
The purpose of the study was to design a flight control system with no pre-determined mathematical model, but instead using a genetic algorithm to maintain the optimal altitude. The study is done through a quantitative empirical research method. In the process of conducting the research, we found that programming a genetic algorithm was cumbersome for novice users to implement. Due to this, we created and released an open-source Python package called EasyGA.
An initial population of 15 chromosomes, 10 genes per chromosome with 100 generations, were used during one trial. The throttle value of the device had an associated gene value of 1 second. A minimum of 30 trials per robot were used to show statistical significance in the study. When the trials were completed, machine learning was achieved. Results showed that optimizing a one degree of freedom(DOF) device, in real-time, is possible without using a pre-determined mathematical model.
Github: https://github.com/danielwilczak101/Evolving-altitude-robot