The problem of carrying a bar-shaped payload suspended by flexible cables attached to two quadrotors is analyzed in this work. The aerial vehicles and the load are dealt with as a single system, whose kinematics is described as a multi-robot formation using the virtual structure approach. The dynamic effects caused by the tethered load over the quadrotors, as well as those caused by each quadrotor over the other, are treated by an adaptive dynamic compensator. To validate the proposal, experiments were run testing the system in adverse conditions: transportation far from quasi-static motion, high payload-to-quadrotor weight ratio, 20% of error in the robot model parameters, transportation under wind disturbances, and payload weight changes during flight. The good performance of the proposed control system in all these tests allows concluding that the proposed system is able to accomplish payload positioning, orientation, and trajectory tracking under adverse conditions, with accelerations up to 1.6 m/s ² .

A substantial interest in aerial robots has grown in recent years. However, the energetic cost of flying is one of the key challenges nowadays. Rotorcrafts are heavier-than-air flying machines that use lift generated by one or several rotors (vertically oriented propellers), and because of this, they spend a large proportion of their available energy to maintain their own weight in the air. In this brief, this concept is used to evaluate the relationship between navigation speed and energy consumption in a miniature quadrotor helicopter, which travels over a desired path. A novel path-following controller is proposed in which the speed of the rotorcraft is a dynamic profile that varies with the geometric requirements of the desired path. The stability of the control law is proved using the Lyapunov theory. The experimental results using a real quadrotor show the good performance of the proposed controller, and the percentages of involved energy are quantified using a model of a lithium polymer battery that was previously identified.

In this paper a complete framework is proposed, to deal with trajectory tracking and positioning with an AR.Drone Parrot quadrotor flying in indoor environments. The system runs in a centralized way, in a computer installed in a ground station, and is based on two main structures, namely a Kalman Filter (KF) to track the 3D position of the vehicle and a nonlinear controller to guide it in the accomplishment of its flight missions. The KF is designed aiming at estimating the states of the vehicle, fusing inertial and visual data. The nonlinear controller is designed with basis on a dynamic model of the AR.Drone, with the closed-loop stability proven using the theory of Lyapunov. Finally, experimental results are presented, which demonstrate the effectiveness of the proposed framework.