This paper investigates the tracking problem of a smooth coordinate-invariant trajectory using dual quaternion algebra. The proposed architecture consists of a cascade structure in which the outer-loop MPC performs real-time smoothing of the manipulator's end-effector twist while an inner-loop kinematic controller ensures tracking of the instantaneous desired end-effector pose. Experiments on a $7$-DoF Franka Emika Panda robotic manipulator validate the proposed method demonstrating its application to constraint the robot twists, accelerations and jerks within prescribed bounds.

For years now, DJI has dominated the consumer drone space. A quick glance at our drone buying guide reveals that half of our picks, including the top three, are all DJI drones. DJI makes excellent products, but there has been a lack of competitors. Until this year, there just haven't been a lot of compelling drones available. Late in 2021, Autel released four drones designed to compete with the entire DJI line.

When building a model, the data scientist can set the value of hyperparameters for the model. Examples of hyperparameters are the number of layers in an artificial neural network, the number of trees in a random forest, etc. The modeler has the power to decide these hyperparameters, providing the flexibility to train the best model. Flexibility comes at the expense of added complexity. So many choices can be overwhelming.

Suppose that we observe noisy linear measurements of an unknown signal that can be modeled as the sum of two component signals, each of which arises from a nonlinear sub-manifold of a high dimensional ambient space. We introduce SPIN, a first order projected gradient method to recover the signal components. Despite the nonconvex nature of the recovery problem and the possibility of underdetermined measurements, SPIN provably recovers the signal components, provided that the signal manifolds are incoherent and that the measurement operator satisfies a certain restricted isometry property. SPIN significantly extends the scope of current recovery models and algorithms for low dimensional linear inverse problems and matches (or exceeds) the current state of the art in terms of performance.