Monica Dragoicea

RO
3papers
4citations
Novelty28%
AI Score15

3 Papers

RODec 10, 2015
Adaptive Neural Control for Mobile Robots Autonomous Navigation

Monica Dragoicea, Ioan Dumitrache, Nicolae Constantin

This paper presents a combined strategy for tracking a non-holonomic mobile robot which works under certain operating conditions for system parameters and disturbances. The strategy includes kinematic steering and velocity dynamics learning of mobile robot system simultaneously. In the learning controller (neural network based controller) the velocity dynamics learning control takes part in tracking of the reference velocity trajectory by learning the inverse function of robot dynamics while the reference velocity control input plays a role in stabilizing the kinematic steering system to the desired reference model of kinematic system even without using the assumption of perfect velocity tracking.

RODec 10, 2015
Mobile Robots Adaptive Control Using Neural Networks

Ioan Dumitrache, Monica Dragoicea

The paper proposes a feed-forward control strategy for mobile robot control that accounts for a non-linear model of the vehicle with interaction between inputs and outputs. It is possible to include specific model uncertainties in the dynamic model of the mobile robot in order to see how the control problem should be addressed taking into consideration the complete dynamic mobile robot model. By means of a neural network feed-forward controller a real non-linear mathematical model of the vehicle can be taken into consideration. The classical velocity control strategy can be extended using artificial neural networks in order to compensate for the modelling uncertainties. It is possible to develop an intelligent strategy for mobile robot control.

RODec 10, 2015
Diversity and Intelligence in Multi-robot Teams

Monica Dragoicea

This research proposes new tools for investigation of behavioral diversity in multi-robot systems and a significant body of results using these tools in simulated and real mobile robot experiments. The experiments specifically describe a framework of defining behavior-based strategies for multi-robot tasks as robot foraging, robot soccer and robot formation. The research focuses specifically on motor schema-based multi-robot systems, which are an important example of behavior-based control.