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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 experiment to faithfully achieve such desired states. This is of particular significance in critical systems, where the vanishing energy gap challenges an adiabatic ground state preparation. We show that a bang-bang protocol, consisting of a time evolution under two different values of an externally tunable parameter, allows for a high-fidelity ground state preparation in evolution times no longer than those required by the application of standard optimal control techniques, such as the chopped-random basis quantum optimization. In addition, owing to their reduced number of variables, such bang-bang protocols are very well suited to optimization purposes, reducing the increasing computational cost of other optimal control protocols. We benchmark the performance of such approach through two paradigmatic models, namely the Landau-Zener and the Lipkin-Meshkov-Glick model. Remarkably, the critical ground state of the latter model can be prepared with a high fidelity in a total evolution time that scales slower than the inverse of the vanishing energy gap.
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 techniques for suppressing such a polarization decoherence in one-way communication channels. We describe in detail the experimental proof of principle of the bang-bang protection technique recently applied to flying qubits in [Damodarakurup et al., Phys. Rev. Lett. 103, 040502]. Bang-bang operations are implemented through appropriately oriented waveplates and dynamical decoupling is shown to be potentially useful to contrast a generic decoherence acting on polarization qubits propagating in dispersive media like, e.g., optical fibers.
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, as measured by the fringe contrast using Ramsey interferometry, is reduced drastically but experiences a weak revival due to in-trap oscillations. The spin current can be controlled using periodic $pi$ pulses (bang-bang control), producing longer spin coherence times. Our results show that spin coherence can be maintained even in the presence of spin currents, with applications to quantum sensing in noisy environments.
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 coordination and control rules. However, it is arduous to verify the global requirements and analyze their performance. This motivates us to pursue a top-down approach, and develop a provable control strategy for deploying a robotic swarm to achieve a desired global configuration. Specifically, we use mean-field partial differential equations (PDEs) to model the swarm and control its mean-field density (i.e., probability density) over a bounded spatial domain using mean-field feedback. The presented control law uses density estimates as feedback signals and generates corresponding velocity fields that, by acting locally on individual robots, guide their global distribution to a target profile. The design of the velocity field is therefore centralized, but the implementation of the controller can be fully distributed -- individual robots sense the velocity field and derive their own velocity control signals accordingly. The key contribution lies in applying the concept of input-to-state stability (ISS) to show that the perturbed closed-loop system (a nonlinear and time-varying PDE) is locally ISS with respect to density estimation errors. The effectiveness of the proposed control laws is verified using agent-based simulations.
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 the path and by looking ahead to account for changes in the path. We propose a novel predictive path-following approach that couples feedforward linearization with path-based model predictive control. Our approach has a few key advantages. By utilizing the differential flatness property, we reduce the path-based model predictive control problem from a nonlinear to a convex optimization problem. Robustness to disturbances is achieved by a dynamic path reference, which adjusts its speed based on the robots progress. We also account for key system constraints. We demonstrate these advantages in experiment on a quadrotor. We show improved performance over a baseline trajectory tracking controller by keeping the quadrotor closer to the desired path under nominal conditions, with an initial offset and under a wind disturbance.