caa a22 4500 463207025 CHVBK 20180405153129.0 cr unu---uuuuu 170326e20070301xx s 000 0 eng 10.1007/s10462-008-9092-3 doi (NATIONALLICENCE)springer-10.1007/s10462-008-9092-3 Bio-inspired behaviour-based control [Elektronische Daten] [Nazmul Siddique, Balasundram Amavasai] The design and development of conventional controllers for robot platforms are sometimes too complex to achieve due to the fact that they require an exact model of the system and of the operating environment. The ability to pre-account for unknown operating environments is an important task for the controller to be robust. In contrast, biological controllers are model free and are based on simple working principles. Due to natural biological principles these controllers are adaptive and more robust than their conventional counterparts. In this paper, a behaviour-based controller has been developed, inspired by the concept of spinal fields found in frogs and rats. The performance of the controller has been verified on a Khepera robot platform. Springer Science+Business Media B.V., 2008 Bio-inspired robotics nationallicence Behaviour-based control nationallicence Sensory fusion nationallicence Spinal field nationallicence Mobile robot nationallicence Siddique Nazmul School of Computing and Intelligent Systems, University of Ulster, BT48 7JL, Londonderry, UK aut Amavasai Balasundram Mechanistic Research, Procter & Gamble, 460 Basingstoke Road, RG2 0QE, Reading, UK aut Artificial Intelligence Review Springer Netherlands 27/2-3(2007-03-01), 131-147 0269-2821 27:2-3<131 2007 27 10462 https://doi.org/10.1007/s10462-008-9092-3 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10462-008-9092-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Siddique Nazmul School of Computing and Intelligent Systems, University of Ulster, BT48 7JL, Londonderry, UK aut NATIONALLICENCE 700 1- Amavasai Balasundram Mechanistic Research, Procter & Gamble, 460 Basingstoke Road, RG2 0QE, Reading, UK aut NATIONALLICENCE 773 0- Artificial Intelligence Review Springer Netherlands 27/2-3(2007-03-01), 131-147 0269-2821 27:2-3<131 2007 27 10462 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer