Lucas Vago Santana

A trajectory tracking and 3D positioning controller for the AR.Drone quadrotor

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.

Outdoor waypoint navigation with the AR.Drone quadrotor

This paper presents a framework to deal with outdoor navigation using an AR.Drone Parrot quadrotor. The proposed system runs in a centralized computer, the ground station, responsible for the communication with the unmanned aerial vehicle (UAV) and for synthesizing the control signals during flight missions. The outdoor navigation is performed through using a layered control architecture, where a high-level control algorithm, designed from the kinematic differential equations describing the movement of the UAV, is used to generate reference signals for a low-level velocity controller. To feedback the controllers, the sensorial data provided by the AR.Drone onboard sensors and a GPS module are fused through a Kalman Filter, allowing getting a more reliable estimate of the UAV state. Finally, experimental results are presented, which demonstrate the effectiveness of the proposed framework.

UAV obstacle avoidance using RGB-D system

This work proposes an obstacle avoidance strategy for UAV navigation in indoor environments. The proposal is able to compute the distance among the UAV and the obstacles (which change their position dynamically), and then to select the closest one. When a collision risk is pointed out, the algorithm establish some escape points, whose orientation is aligned tangentially to the obstacle edge or to the UAV normal displacement. Considering that only the desired point is change during the avoidance maneuver, the stability of the whole nonlinear system is demonstrated in the sense of Lyapunov. Information Filter is used to track the 3D positioning of the UAV and the obstacles. Moreover, UAV state variables are given by a Decentralized Information Filter, which fuses information from the Inertial Measurement Unit onboard the aircraft and the depth-camera sensor (RGB-D). The effectiveness of the proposal is demonstrated by simulation results, which take into account the AR.drone rotorcraft dynamic model.

Estimating and controlling UAV position using RGB-D/IMU data fusion with decentralized information/Kalman filter

This paper proposes a 3D data capture system, based on the fusion of data coming from an active depth sensor and a inertial measurement unit (IMU), to determine the position of an aerial unmanned vehicle (UAV) in indoor environments, for control purposes. Firstly, the method adopted to detect the vehicle through using a sequence of RGB-D images. After that, the information provided by the active depth sensor is fused with the data provided by the IMU onboard the vehicle, using a Decentralized Kalman Filter (DKF) and a Decentralized Information Filter (DIF), whose performance are compared. In the sequel, a nonlinear controller is used for positioning the UAV. Finally, the performance differences between the DKF and the DIF are highlighted, as well as the divergence between the results of the depth sensor and the inertial one, in experiments involving abrupt maneuvers to induce estimation errors in the inertial unit, to check the effectiveness of the developed 3D data capture system.

Estimation and control of the 3D position of a quadrotor in indoor environments

This paper presents a method for the estimation and control of the 3D position of a quadrotor in indoor environments, which can stabilize the aircraft hovering over a reference as well as follow a moving reference. The proposed approach adopts the Extended Kalman Filter - EKF - as framework to fuse the information provided by the sensors available onboard the quadrotor, and a PD controller to regulate the flight. Experimental results are also presented, aiming at demonstrating the effectiveness of the proposed method.

Navigation and Cooperative Control Using the AR.Drone Quadrotor

This paper presents a computational system designed to perform autonomous indoor flights using low-cost equipment. Depending on the mission to be accomplished, one or two Parrot AR.Drone 2.0 quadrotors are supposed to fly in a three-dimensional workspace, guided by the navigation algorithms embedded in the proposed framework, which runs in a ground control computer. The tasks addressed involve positioning, trajectory tracking and leader-follower formation control. The key techniques required to solve such problems are reported in topics, including the mathematic modelling of the quadrotor, a model-based nonlinear flight controller and a state estimation strategy for sensory data fusion. The framework embeds the last two subsystems just mentioned, plus a communication link between the ground computer and the aircraft, to read the sensory data and to send the control signals necessary to guide the vehicle. Some experimental results are also presented and discussed, which allow concluding that the proposed methods are efficient in accomplishing the tasks addressed.

An automatic flight control system for the AR.Drone quadrotor in outdoor environments

This paper describes the application of a low-cost quadrotor testbed in real outdoor flights. Along the text it is discussed how to design a flight control system, including methods for fusing out-of-sequence sensor measurements, for modelling the aircraft and for designing a high level flight controller. To validate the proposed system, some flights were performed, executing the specific tasks of positioning and trajectory tracking. The experimental results, discussed in the text and presented in videos, demonstrate the effectiveness of such a system.

Hovering control of a miniature helicopter attached to a platform

In recent years, many researchers have been working in unmanned aerial vehicle to execute a large variety of tasks. The first step to reach it is the development of a sensing and communication structure capable to control such vehicles. In this context, this work presents a system designed to control a miniature helicopter during an autonomous hover flight in a platform. The proposed structure allows getting and transmitting data related to the current state of the aircraft to a ground station and transmit the control signals necessary to command it during the flight. A fusion algorithm and the strategy used to obtain the helicopter orientation using an Inertial Measurement Unit (EMU) is also presented. Finally, experimental results are presented and discussed, validating the proposal.

Heterogeneous leader-follower formation based on kinematic models

This paper proposes a control structure for a heterogeneous leader-follower formation involving an unmanned ground vehicle (UGV) - the leader, and an unmanned aerial vehicle (UAV) - the follower, based on their kinematic models. The control architecture is a centralized one, in which it is possible to get the robots sensory data and to synthesize and deliver the control signals necessary to establish the formation. The experimental platform, the mathematical models of the robots and other structures needed to carry out the cooperative navigation are detailed. The closed-loop system stability is demonstrated and the effectiveness of the proposed methodology is validated through experimental results.