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Robot Control
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This research area focuses on the design, development and implementation of control systems for wheeled mobile robots and robotic manipulators in order to enhance their capabilities through increased levels of accuracy and autonomy. Control algorithms developed in the department include algorithms for trajectory tracking of mobile robots in conjunction with obstacle avoidance, as well as novel neural network-based algorithms for stochastic-adaptive dynamic control of mobile robots where the controller estimates the nonlinear dynamics of the robot in real-time, so that it can maneuver successfully even in those situations when these are uncertain or time-varying. In contrast to other adaptive controllers hitherto proposed for mobile robots, these algorithms fuse estimation and control via the concept of dual-adaptive control that leads to improved performance.

 

PowerBot_1
RoboticManipulator_2

The PowerBot mobile Robot 

 

    Robotic Manipulators available in the labarotatories

 

Further research in this area includes the investigation of various Simultaneous-Localization and Mapping (SLAM) algorithms to enable a wheeled mobile robot to build a map of its environment while simultaneously and continuously estimating its location within this map. Both passive and active SLAM algorithms have been considered via simulations and physical experiments using PowerBot, a commercial mobile robot available in our laboratory, that is fitted with several high-end sensors including a laser range-finder and sonar belt.  The department also conducts research projects on trajectory control of robotic manipulators based on visual feedback through camera inputs, as well as active collaboration with colleagues at the University of Le Havre in France on the design of visual-tracking control algorithms for mobile robots.
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Last Updated: 5 March 2014

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