Genetic Algorithm Evolving Robot

prusaprinters

<p>Full medium article can be found here:&nbsp;</p><p><a href="https://towardsdatascience.com/genetic-algorithm-robot-evolving-altitude-using-python-c-and-an-arduino-1acf4cf98d63">https://towardsdatascience.com/genetic-algorithm-robot-evolving-altitude-using-python-c-and-an-arduino-1acf4cf98d63</a></p><p>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 <a href="https://github.com/danielwilczak101/EasyGA">EasyGA</a>.</p><p>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.</p><p>&nbsp;</p><p>Github: <a href="https://github.com/danielwilczak101/Evolving-altitude-robot">https://github.com/danielwilczak101/Evolving-altitude-robot</a></p><p>&nbsp;</p>