Empirical Control System Development for Intelligent Mobile Robot Based on the Elements of the Reinforcement Machine Learning and Axiomatic Design Theory
Апстракт
This paper presents the authors’ efforts to conceptual design of control
system that can learn from its own experience. The ability of adaptive
behaviour regarding the given task in real, unpredictable conditions is one
of the main demands for every intelligent robotic system. To solve this
problem, the authors suggest a learning approach that combines empirical
control strategy, reinforcement learning and axiomatic design theory. The
proposed concept uses best features of mentioned theoretical approaches
to produce optimal action in the current state of the mobile robot. In this
paper empirical control theory imparts the basis of conceptual solution for
the navigation problem of mobile robot. Reinforcement learning enables
the mechanisms that memorize and update environment responses, and
combining with the empirical control theory determines best possible
action according to the present circumstances. Axiomatic design theory
accurately defines the problem and possible so...lution for the given task in
terms of the elements defined by two previously mentioned approaches.
Part of the proposed algorithm was implemented on the LEGO
Mindstorms NXT mobile robot for the navigation task in an unknown
manufacturing environment. Experimental results have shown good
perspective for development of efficient and adaptable control system,
which could lead to autonomous mobile robot behaviour.
Кључне речи:
learning mobile robot / empirical control theory / reinforcement learning / axiomatic design theory / mobile robot navigationИзвор:
FME Transactions, New Series, 2011, 39, 1, 1-8Издавач:
- University of Belgrade - Faculty of Mechanical Engineering
Финансирање / пројекти:
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Mitić, Marko AU - Miljković, Zoran AU - Babić, Bojan PY - 2011 UR - https://machinery.mas.bg.ac.rs/handle/123456789/3962 AB - This paper presents the authors’ efforts to conceptual design of control system that can learn from its own experience. The ability of adaptive behaviour regarding the given task in real, unpredictable conditions is one of the main demands for every intelligent robotic system. To solve this problem, the authors suggest a learning approach that combines empirical control strategy, reinforcement learning and axiomatic design theory. The proposed concept uses best features of mentioned theoretical approaches to produce optimal action in the current state of the mobile robot. In this paper empirical control theory imparts the basis of conceptual solution for the navigation problem of mobile robot. Reinforcement learning enables the mechanisms that memorize and update environment responses, and combining with the empirical control theory determines best possible action according to the present circumstances. Axiomatic design theory accurately defines the problem and possible solution for the given task in terms of the elements defined by two previously mentioned approaches. Part of the proposed algorithm was implemented on the LEGO Mindstorms NXT mobile robot for the navigation task in an unknown manufacturing environment. Experimental results have shown good perspective for development of efficient and adaptable control system, which could lead to autonomous mobile robot behaviour. PB - University of Belgrade - Faculty of Mechanical Engineering T2 - FME Transactions, New Series T1 - Empirical Control System Development for Intelligent Mobile Robot Based on the Elements of the Reinforcement Machine Learning and Axiomatic Design Theory EP - 8 IS - 1 SP - 1 VL - 39 UR - https://hdl.handle.net/21.15107/rcub_machinery_3962 ER -
@article{ author = "Mitić, Marko and Miljković, Zoran and Babić, Bojan", year = "2011", abstract = "This paper presents the authors’ efforts to conceptual design of control system that can learn from its own experience. The ability of adaptive behaviour regarding the given task in real, unpredictable conditions is one of the main demands for every intelligent robotic system. To solve this problem, the authors suggest a learning approach that combines empirical control strategy, reinforcement learning and axiomatic design theory. The proposed concept uses best features of mentioned theoretical approaches to produce optimal action in the current state of the mobile robot. In this paper empirical control theory imparts the basis of conceptual solution for the navigation problem of mobile robot. Reinforcement learning enables the mechanisms that memorize and update environment responses, and combining with the empirical control theory determines best possible action according to the present circumstances. Axiomatic design theory accurately defines the problem and possible solution for the given task in terms of the elements defined by two previously mentioned approaches. Part of the proposed algorithm was implemented on the LEGO Mindstorms NXT mobile robot for the navigation task in an unknown manufacturing environment. Experimental results have shown good perspective for development of efficient and adaptable control system, which could lead to autonomous mobile robot behaviour.", publisher = "University of Belgrade - Faculty of Mechanical Engineering", journal = "FME Transactions, New Series", title = "Empirical Control System Development for Intelligent Mobile Robot Based on the Elements of the Reinforcement Machine Learning and Axiomatic Design Theory", pages = "8-1", number = "1", volume = "39", url = "https://hdl.handle.net/21.15107/rcub_machinery_3962" }
Mitić, M., Miljković, Z.,& Babić, B.. (2011). Empirical Control System Development for Intelligent Mobile Robot Based on the Elements of the Reinforcement Machine Learning and Axiomatic Design Theory. in FME Transactions, New Series University of Belgrade - Faculty of Mechanical Engineering., 39(1), 1-8. https://hdl.handle.net/21.15107/rcub_machinery_3962
Mitić M, Miljković Z, Babić B. Empirical Control System Development for Intelligent Mobile Robot Based on the Elements of the Reinforcement Machine Learning and Axiomatic Design Theory. in FME Transactions, New Series. 2011;39(1):1-8. https://hdl.handle.net/21.15107/rcub_machinery_3962 .
Mitić, Marko, Miljković, Zoran, Babić, Bojan, "Empirical Control System Development for Intelligent Mobile Robot Based on the Elements of the Reinforcement Machine Learning and Axiomatic Design Theory" in FME Transactions, New Series, 39, no. 1 (2011):1-8, https://hdl.handle.net/21.15107/rcub_machinery_3962 .