In this paper, we present a Perception-constrained Nonlinear Model Predictive Control (NMPC) framework for the real-time control of multi-rotor aerial vehicles. Our formulation considers both constraints from a perceptive sensor and realistic actuator limitations that are the rotor minimum and maximum speeds and accelerations. The formulation is meant to be generic and considers a large range of multi-rotor platforms (such as underactuated quadrotors or tilted-propellers hexarotors) since it does not rely on differential flatness for the dynamical equations, and a broad range of sensors, such as cameras, lidars, etc... The perceptive constraints are expressed to maintain visibility of a feature point in the sensor's field of view, while performing a reference maneuver. We demonstrate both in simulation and real experiments that our framework is able to exploit the full capabilities of the multi-rotor, to achieve the motion under the aforementioned constraints, and control in real-time the platform at a motor-torque level, avoiding the use of an intermediate unconstrained trajectory tracker.

In this paper, we present a general control architecture that allows fully-actuated aerial robots to autonomously accomplish tasks that require both perception and physical interaction with the external environment. We integrate the novel Flying End-Effector paradigm and a Hybrid Visual Servoing (HVS) scheme to design a general control architecture for fully-actuated aerial robots. Thanks to the proposed solution, a fully-actuated aerial robot can autonomously accomplish tasks that require both perception and physical interaction without resorting to any external force/torque sensor. The control architecture is entirely described, features a wrench observer and an admittance filter, and is subsequently validated on real experiments. The code for the proposed control architecture is provided open-source.