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Robot Control Code Generation by Task Demonstration in a Dynamic Environment

Gardiner, Bryan, Coleman, SA, McGinnity, TM and He, H (2012) Robot Control Code Generation by Task Demonstration in a Dynamic Environment. Robotics and Autonomous Systems, Elsevier (Academic Press), UNSPECIFIED pp, DOI: 10.1016/j.robot.2012.07.023 [Internet publication]

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DOI: 10.1016/j.robot.2012.07.023


Generally within mobile robotics, the most dominant relationship to consider when implementing robot control code is the one between the robot’s sensors and its motors. When implementing such a relationship, efficiency and reliability are of crucial importance. The latter aspects often prove challenging due to the complex interaction between a robot and the environment in which it exists, frequently resulting in a time consuming iterative process where control code is redeveloped and tested many times before obtaining an optimal controller. In this paper, we address this challenge by implementing an alternative approach to control code generation, which first identifies the desired robot behaviour and represents the sensor-motor task algorithmically through system identification using the NARMAX modelling methodology. The control code is generated by task demonstration, where the sensory perception and velocities are logged and the relationship that exists between them is then modelled using system identification. This approach produces transparent control code through non-linear polynomial equations that can be mathematically analysed to obtain formal statements regarding specific inputs/outputs. We demonstrate this approach to control code generation and analyse its performance in dynamic environments.

Item Type:Internet publication
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:22928
Deposited By: Professor Martin McGinnity
Deposited On:14 Sep 2012 10:52
Last Modified:14 Sep 2012 10:52

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