Control structure for a car-like robot using artificial neural networks and genetic algorithms
| Autor: | João Maurício Rosário, Camilo Andrés Cáceres Flórez, Dario Amaya |
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| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Neuroevolution Artificial neural network Computer science Control (management) Control engineering 02 engineering and technology Kinematics Network topology 020901 industrial engineering & automation Artificial Intelligence Control theory Position (vector) Genetic algorithm 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Software |
| Zdroj: | Neural Computing and Applications. 32:15771-15784 |
| ISSN: | 1433-3058 0941-0643 |
| DOI: | 10.1007/s00521-018-3514-1 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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