Adaptive neural networks for mobile robotic control

Jeff R. Burnett; Cihan H. Dagli

doi:10.1117/12.421176

21 March 2001 Adaptive neural networks for mobile robotic control

Jeff R. Burnett, Cihan H. Dagli

Proceedings Volume 4390, Applications and Science of Computational Intelligence IV; (2001) https://doi.org/10.1117/12.421176
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

Abstract

Movement of a differential drive robot has non-linear dependence on the current position and orientation. A controller must be able to deal with the non-linearity of the plant. The controller must either linearize the plant and deal with special cases, or be non-linear itself. Once the controller is designed, implementation on a real robotic platform presents challenges due to the varying parameters of the plant. Robots of the same model may have different motor frictions. The surface the robot maneuvers on may change e.g. carpet to tile. Batteries will drain, providing less power over time. A feed-forward neural network controller could overcome these challenges. The network could learn the non- linearities of the plant and monitor the error for parameter changes and adapt to them. In this manner, a single controller can be designed for an ideal robot, and then used to populate a multi-robot colony without manually fine tuning the controller for each robot. This paper shall demonstrate such a controller, outlining design in simulation and implementation on Khepera robotic platforms.

Citation Download Citation

Jeff R. Burnett and Cihan H. Dagli "Adaptive neural networks for mobile robotic control", Proc. SPIE 4390, Applications and Science of Computational Intelligence IV, (21 March 2001); https://doi.org/10.1117/12.421176

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KEYWORDS

Neural networks

Filtering (signal processing)

Robotics

Device simulation

Electronic filtering

Control systems

Visualization

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