We present a control methodology for underactuated aerial manipulators that is both easy to implement on real systems and able to achieve highly dynamic behaviors. The method is composed by two parts: i) a nominal input/state trajectory generator that takes into account the full-body dynamics of the system exploiting its differential flatness property; ii) a decentralized feedback controller acting on the actuated degrees of freedom that confers the needed robustness to the closed-loop system. We demonstrate that the proposed controller is able to precisely track dynamic trajectories when implemented on a standard hardware. Comparative experiments clearly show the benefit of using the nominal input/state generator.

We present an optimization framework for the design and analysis of underactuated biped walkers, characterized by passive or actuated joints with rigid or non-negligible elastic actuation/transmission elements. The framework is based on optimal control, dealing with geometric constraints and various dynamic objective functions, as well as boundary conditions, which helps in selecting optimal values both for the actuation and the transmission parameters. Solutions of the formulated problems are shown for different kinds of bipedal architectures, and comparisons drawn between traditional rigid robots and compliant ones show the energy-efficiency of compliant actuators in the context of locomotion.

We address the problem of estimating an external wrench acting along the structure of a robot manipulator, together with the contact position where the external force is being applied. For this, we consider the combined use of a force/torque sensor mounted at the robot base and of a model-based virtual sensor. The virtual sensor is provided by the residual vector commonly used for collision detection and isolation in human-robot interaction. Integrating the two types of measurement tools provides an efficient way to estimate all unknown quantities, using also the recursive Newton-Euler algorithm for dynamic computations. Different operative conditions are considered, including the special cases of pointwise interaction (pure contact force), known contact location, and of a base sensor measuring only forces. We highlight also the conditions for a correct estimation to be fully virtual, i.e., without resorting to a force/torque sensor. Realistic simulations assess the estimation performance for a 7R robot in motion, subject to an unknown external force applied to an unknown location.

In this paper we introduce a particularly relevant class of aerial manipulators that we name protocentric. These robots are formed by an underactuated aerial vehicle, a planar-Vertical Take-Off and Landing (PVTOL), equipped with any number of different parallel manipulator arms with the only property that all the first joints are attached at the Center of Mass (CoM) of the PVTOL, while the center of actuation of the PVTOL can be anywhere. We prove that protocentric aerial manipulators (PAMs) are differentially flat systems regardless the number of joints of each arm and their kinematic and dynamic parameters. The set of flat outputs is constituted by the CoM of the PVTOL and the absolute orientation angles of all the links. The relative degree of each output is equal to four. More amazingly, we prove that PAMs are differentially flat even in the case that any number of the joints are elastic, no matter the internal distribution between elastic and rigid joints. The set of flat outputs is the same but in this case the total relative degree grows quadratically with the number of elastic joints. We validate the theory by simulating object grasping and transportation tasks with unknown mass and parameters and using a controller based on dynamic feedback linearization.

A model predictive controller is developed for following the position of a human dancer in robot ballroom dancing. The control design uses a dynamic model of a dancer, based on a variant of the so-called 3D Linear Inverted Pendulum Mode that includes also the swing foot. This model serves as a basis for a Kalman predictor of the human motion during the single-support phase, while a simpler kinematic technique is used during the double-support phase. The output of the prediction filter enables to design a Model Predictive Control (MPC) law, by recursively solving on line and within a preview window a convex linear-quadratic optimization problem, constrained by differential kinematic bounds on robot commands. Two different control strategies, either at the velocity or at the acceleration level, are proposed and compared in simulations and in actual experiments. Accurate and reactive behaviors are obtained by the ballroom robot follower, confirming the benefit of the predictive/filtering nature of a MPC approach to handle uncertainty of human intentions and noisy signals.

We consider the problem of computing the inverse dynamics of a serial robot manipulator with N elastic joints in a recursive numerical way. The solution algorithm is a generalized version of the standard Newton-Euler approach, running still with linear complexity O(N) but requiring to set up recursions that involve higher order derivatives of motion and force variables. Mimicking the case of rigid robots, we use this algorithm and a numerical factorization of the link inertia matrix (which needs to be inverted in the elastic joint case) for implementing on-line a feedback linearization control law for trajectory tracking purposes. The complete method has a complexity that grows as O(N3). The developed tools are generic, easy to use, and do not require symbolic Lagrangian modeling and customization, thus being of particular interest when the number N of elastic joints becomes large.