Neuroevolution and NEAT at Evolving Neurocontrollers for Obstacle Avoidance for a Robot Arm [Articol]
| dc.contributor.author | Darii, Andrei | en |
| dc.contributor.author | Nistor, Marian Sorin | en |
| dc.contributor.author | Pickl, Stefan | en |
| dc.date.accessioned | 2025-07-03T07:04:26Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | This paper presents a comparative analysis of Neuroevolution and NEAT algorithms for evolving neural controllers capable of evading obstacles. The fitness criterion is the number of time steps a robot is able to avoid a moving obstacle that increases in speed over time. The experiments simulate the WLkata Mirobot, a small 6-joint robot, focusing on obstacle evasion using forward kinematics. The results show the average number of generations required by each algorithm to achieve a certain fitness and analyze the average fitness per generation. This comparison offers insights into the effectiveness and efficiency of each algorithm in optimising robotic obstacle avoidance. | en |
| dc.identifier.citation | DARII, Andrei; Marian Sorin NISTOR and Stefan PICKL. Neuroevolution and NEAT at Evolving Neurocontrollers for Obstacle Avoidance for a Robot Arm. In: International Conference dedicated to the 60th anniversary of the foundation of Vladimir Andrunachievici Institute of Mathematics and Computer Science, MSU, October 10-13 2024. Chisinau: [S. n.], 2024, pp. 280-286. ISBN 978-9975-68-515-3. | en |
| dc.identifier.isbn | 978-9975-68-515-3 | |
| dc.identifier.uri | https://msuir.usm.md/handle/123456789/18261 | |
| dc.language.iso | en | |
| dc.subject | genetic algorithm | en |
| dc.subject | neuroevolution | en |
| dc.subject | NEAT | en |
| dc.subject | robot arm | en |
| dc.subject | obstacle avoidance | en |
| dc.title | Neuroevolution and NEAT at Evolving Neurocontrollers for Obstacle Avoidance for a Robot Arm [Articol] | en |
| dc.type | Article |