Control structure for a car-like robot using artificial neural networks and genetic algorithms
Published in Neural Computing and Applications, 2018
Recommended citation: Caceres Florez, C.A., Rosario, J.M. & Amaya, D. Neural Comput & Applic (2018).https://doi.org/10.1007/s00521-018-3514-1 https://link.springer.com/article/10.1007%2Fs00521-018-3514-1
The idea of improving human s life quality by making life more comfortable and easy is nowadays possible using current technologies and techniques to solve complex daily problems. The presented idea in this work proposes a control strategy for autonomous robotic systems, specifically car-like robots. The main objective of this work is the development of a reactive navigation controller by means of obstacles avoidance and position control to reach a desired position in an unknown environment. This research goal was achieved by the integration of potential fields and neuroevolution controllers. The neuro-evolutionary controller was designed using the (NEAT) algorithm -Neuroevolution of Augmented Topologies- and trained using a designed training environment. The methodology used allowed the vehicle to reach a certain level of autonomy, obtaining a stable controller that includes kinematic and dynamic considerations. The obtained results showed significant improvements compared to the comparison work.
Recommended citation: Caceres Florez, C.A., Rosario, J.M. & Amaya, D. Neural Comput & Applic (2018).https://doi.org/10.1007/s00521-018-3514-1