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We show a control algorithm to guide a robotic walking assistant along a planned path. The control strategy exploits the electromechanical brakes mounted on the back wheels of the walker. In order to reduce the hardware requirements we adopt a Bang Bang approach relying of four actions (with saturated value for the braking torques).When the platform is far away from the path, we execute an approach phase in which the walker converges toward the platform with a specified angle. When it comes in proximity of the platform, the control strategy switches to a path tracking mode, which uses the four control actions to converge toward the path with an angle which is a function of the state. This way it is possible to control the vehicle in feedback, secure a gentle convergence of the user to the planned path and her steady progress towards the destination.
The fast and faithful preparation of the ground state of quantum systems is a challenging task but crucial for several applications in the realm of quantum-based technologies. Decoherence poses a limit to the maximum time-window allowed to an experim
In the propagation of optical pulses through dispersive media, the frequency degree of freedom acts as an effective decohering environment on the polarization state of the pulse. Here we discuss the application of open-loop dynamical-decoupling techn
We generate spin currents in an $^{87}$Rb spin-2 Bose-Einstein condensate by application of a magnetic field gradient. The spin current destroys the spin polarization, leading to a sudden onset of two-body collisions. In addition, the spin coherence,
With the rapid development of AI and robotics, transporting a large swarm of networked robots has foreseeable applications in the near future. Existing research in swarm robotics has mainly followed a bottom-up philosophy with predefined local coordi
For many tasks, predictive path-following control can significantly improve the performance and robustness of autonomous robots over traditional trajectory tracking control. It does this by prioritizing closeness to the path over timed progress along