Achieving closed-loop control over wireless is crucial in realizing the vision of Industry 4.0 and beyond. This demonstration shows the viability of closed-loop control over wireless through a high-performance wireless solution. The closed-loop control problem involves remote balancing of a two-wheeled robot that represents an inverted pendulum on wheels.
Balancing is a fundamental need for legged robots due to their unstable floating-base nature. Balance control has been thoroughly studied for simple models such as the linear inverted pendulum thanks to the concept of the instantaneous capture point (ICP), yet the constant center of mass height assumption limits the application. This paper explores balancing of the variable-height inverted pendulum (VHIP) model by introducing the emph{instantaneous capture input} (ICI), an extension of the ICP based on its key properties. Namely, the ICI can be computed as a function of the state, and when this function is used as the control policy, the ICI is rendered stationary and the system will eventually come to a stop. This characterization induces an analytical region of capturable states for the VHIP, which can be used to conceptually guide where to step. To further address state and control constraints during recovery, we present and theoretically analyze an explicit ICI-based controller with online optimal feedback gains. Simulations demonstrate the validity of our controller for capturability maintenance compared to an approach based on the divergent component of motion.
A Python module for rapid prototyping of constraint-based closed-loop inverse kinematics controllers is presented. The module allows for combining multiple tasks that are resolved with a quadratic, nonlinear, or model predictive optimization-based approach, or a set-based task-priority inverse kinematics approach. The optimization-based approaches are described in relation to the set-based task approach, and a novel multidimensional in tangent cone function is presented for set-based tasks. A ROS component is provided, and the controllers are tested with matching a pose using either transformation matrices or dual quaternions, trajectory tracking while remaining in a bounded workspace, maximizing manipulability during a tracking task, tracking an input markers position, and force compliance.
This paper proposes a real-time approach for long-term inertial navigation based only on an Inertial Measurement Unit (IMU) for self-localizing wheeled robots. The approach builds upon two components: 1) a robust detector that uses recurrent deep neural networks to dynamically detect a variety of situations of interest, such as zero velocity or no lateral slip; and 2) a state-of-the-art Kalman filter which incorporates this knowledge as pseudo-measurements for localization. Evaluations on a publicly available car dataset demonstrates that the proposed scheme may achieve a final precision of 20 m for a 21 km long trajectory of a vehicle driving for over an hour, equipped with an IMU of moderate precision (the gyro drift rate is 10 deg/h). To our knowledge, this is the first paper which combines sophisticated deep learning techniques with state-of-the-art filtering methods for pure inertial navigation on wheeled vehicles and as such opens up for novel data-driven inertial navigation techniques. Moreover, albeit taylored for IMU-only based localization, our method may be used as a component for self-localization of wheeled robots equipped with a more complete sensor suite.
Closed-loop control of turbulent flows is a challenging problem with important practical and fundamental implications. We perform closed-loop control of forced, turbulent jets based on a wave-cancellation strategy. The study is motivated by the success of recent studies in applying wave cancellation to control instability waves in transitional boundary layers and free-shear flows. Using a control law obtained through a system-identification technique, we successfully implement wave-cancellation-based, closed-loop control, achieving order-of-magnitude attenuations of velocity fluctuations. Control is shown to reduce fluctuation levels over an extensive streamwise range.
We present experimental evidence of the successful closed-loop optimization of the dynamics of cold atoms in an optical lattice. We optimize the loading of an ultracold atomic gas minimizing the excitations in an array of one-dimensional tubes (3D-1D crossover) and we perform an optimal crossing of the quantum phase-transition from a Superfluid to a Mott-Insulator in a three-dimensional lattice. In both cases we enhance the experiment performances with respect to those obtained via adiabatic dynamics, effectively speeding up the process by more than a factor three while improving the quality of the desired transformation.
Aleksandar Stanoev
,Adnan Aijaz
,Anthony Portelli
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(2020)
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"Demo: Closed-Loop Control over Wireless -- Remotely Balancing an Inverted Pendulum on Wheels"
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Adnan Aijaz
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