Empirical Control System for Robots That Learn
Нема приказа
Конференцијски прилог (Објављена верзија)
,
Prof. K.-D. Bouzakis
Метаподаци
Приказ свих података о документуАпстракт
Autonomous behaviour of industrial robot is based on the ability of machine learning. Unlike natural systems which could learn on the basis of experience, artificial systems are thought to be unable to do so for a long time. However, the concept of empirical control realizes the ability of machine learning on the basis of experience. This paper shows the algorithm of empirical control which presents the key for establishing developed empirical control system. The validity of established empirical control system pointed out that, after machine learning by using artificial neural networks, specially designed robot named "Don Kihot" approaches directly towards the work-piece. Developed software subsystems as well as experimental results of machine learning are presented in this paper.
Кључне речи:
Robot / Empirical control system / Machine learning / Artificial neural networks / Autonomous robot behaviorИзвор:
Proceedings of the 1st International Conference on Manufacturing Engineering (ICMEN 2002) and EUREKA Brokerage Event, 2002, 759-768Колекције
Институција/група
Mašinski fakultetTY - CONF AU - Miljković, Zoran PY - 2002 UR - https://machinery.mas.bg.ac.rs/handle/123456789/4966 AB - Autonomous behaviour of industrial robot is based on the ability of machine learning. Unlike natural systems which could learn on the basis of experience, artificial systems are thought to be unable to do so for a long time. However, the concept of empirical control realizes the ability of machine learning on the basis of experience. This paper shows the algorithm of empirical control which presents the key for establishing developed empirical control system. The validity of established empirical control system pointed out that, after machine learning by using artificial neural networks, specially designed robot named "Don Kihot" approaches directly towards the work-piece. Developed software subsystems as well as experimental results of machine learning are presented in this paper. C3 - Proceedings of the 1st International Conference on Manufacturing Engineering (ICMEN 2002) and EUREKA Brokerage Event T1 - Empirical Control System for Robots That Learn EP - 768 SP - 759 UR - https://hdl.handle.net/21.15107/rcub_machinery_4966 ER -
@conference{ author = "Miljković, Zoran", year = "2002", abstract = "Autonomous behaviour of industrial robot is based on the ability of machine learning. Unlike natural systems which could learn on the basis of experience, artificial systems are thought to be unable to do so for a long time. However, the concept of empirical control realizes the ability of machine learning on the basis of experience. This paper shows the algorithm of empirical control which presents the key for establishing developed empirical control system. The validity of established empirical control system pointed out that, after machine learning by using artificial neural networks, specially designed robot named "Don Kihot" approaches directly towards the work-piece. Developed software subsystems as well as experimental results of machine learning are presented in this paper.", journal = "Proceedings of the 1st International Conference on Manufacturing Engineering (ICMEN 2002) and EUREKA Brokerage Event", title = "Empirical Control System for Robots That Learn", pages = "768-759", url = "https://hdl.handle.net/21.15107/rcub_machinery_4966" }
Miljković, Z.. (2002). Empirical Control System for Robots That Learn. in Proceedings of the 1st International Conference on Manufacturing Engineering (ICMEN 2002) and EUREKA Brokerage Event, 759-768. https://hdl.handle.net/21.15107/rcub_machinery_4966
Miljković Z. Empirical Control System for Robots That Learn. in Proceedings of the 1st International Conference on Manufacturing Engineering (ICMEN 2002) and EUREKA Brokerage Event. 2002;:759-768. https://hdl.handle.net/21.15107/rcub_machinery_4966 .
Miljković, Zoran, "Empirical Control System for Robots That Learn" in Proceedings of the 1st International Conference on Manufacturing Engineering (ICMEN 2002) and EUREKA Brokerage Event (2002):759-768, https://hdl.handle.net/21.15107/rcub_machinery_4966 .