Alexandre S. Brandao

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.

High-level underactuated nonlinear control for rotorcraft machines

This paper proposes a high-level underactuated nonlinear controller capable to guide a RUAV during a 3D flight. First, it presents a dynamic model to represent the dynamics of the aircraft, explicitly showing its underactuated character. Following, a suitable controller based on partial feedback linearization is designed for stabilizing the rotorcraft dynamics. A proof of the stability of the closed-loop control system in the sense of Lyapunov, including modeled disturbances and parametric errors, is also presented, as well as experimental results obtained with a quadrotor, which validate the proposed model and controller.

Decentralized control of leader-follower formations of mobile robots with obstacle avoidance

This paper proposes a decentralized control scheme to guide a leader-follower formation of unicycle-like mobile robots to pass between static obstacles, demanding just one controller per robot. Two approaches are discussed, in terms of obstacle avoidance. One considers the whole formation as a virtual robot, which should avoid obstacles and keep the formation aspect. To do that, the leader robot takes care of goal seeking and obstacle avoidance, while the follower one just keeps the formation as a whole rigid body (rigid formation). In the second approach, the follower robot keeps only its separation from the leader (semi-rigid formation) and avoids obstacles, while the leader one seeks for the goal and avoids obstacles. In each case, the controllers onboard the robots do not share information during navigation (the control strategy is a decentralized one). Experimental results validating the proposal are also presented and discussed.

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.

A Multi-Layer Control Scheme for a centralized UAV formation

This paper presents an application of the multi-layer control scheme to guide a formation of three unmanned aerial vehicles (UAV) in trajectory tracking missions. In such case, each part of the formation control problem is dealt by an individual layer, which is independent module dealing with a specific part of the navigation problem. These layers are responsible to generate the desired path of the formation, to provide the desired posture of the robots, and to establish the control signal of each robot to reach their desired positions. The formation controller here introduced is able to coordinate the robots to the desired formation, including the possibility of time-varying position and/or shape, while a nonlinear underactuated controller previously proposed is responsible to guide the UAVs to their desired positions. The stability analysis of the closed-loop system is demonstrated in the sense of Lyapunov, resulting that the formation errors are ultimately bounded. Finally, simulation results for a group of three quadrotors are presented and discussed to validate the proposed model and controller.

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.

Embedding obstacle avoidance in the control of a flexible multi-robot formation

This work proposes a way to make a group of mobile robots which are navigating as a formation to avoid obstacles in their paths. A trajectory tracking controller is proposed to guide the robots during the navigation, to which an obstacle deviation subsystem is added. Such subsystem is implemented in each robot in the formation, whereas the proposed controller is applicable to subsets of three robots. The obstacle deviation is based on virtual forces, which are considered to change the velocities of the individual robots. A proof of the stability of the control system implemented with the proposed controller, including control signal saturation to avoid the saturation of the actuators, is presented, based on the theory of Lyapunov. Simulated and experimental results are also presented, which validate the proposal.

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.

3-D path-following with a miniature helicopter using a high-level nonlinear underactuated controller

This work proposes a controller to guide a miniature helicopter during a 3D path-following task, emphasizing its dynamic stabilization, while reaching a set of reference poses. The proposal includes a nonlinear underactuated control strategy previously proposed for the UAV, implemented as an inner closed loop, to stabilize the helicopter in a given reference pose, as well a path-following controller based on the kinematic model of the rotorcraft implemented as an outer closed loop. A proof of the stability of the equilibrium of the whole closed-loop system in the sense of Lyapunov is also presented. Finally, simulation results are presented and discussed, which validate the proposed controller.

Decentralized Control of a Formation Involving a Miniature Helicopter and a Team of Ground Robots Based on Artificial Vision

This work addresses a coordinate control scheme for a helicopter and a formation of ground robots, based on artificial vision. First, a control scheme previously proposed is implemented to guide a formation of three ground mobile robots while they track a desired trajectory. In the sequel, a nonlinear controller based on the inverse dynamics of the aerial vehicle, focusing on its under actuated character, is used to guide its navigation. The mission to be accomplished by the helicopter consists in tracking the centroid of the ground formation. An important aspect to be emphasized is that the two controllers adopted operate totally independently, thus characterizing a decentralized control scheme. An artificial vision system onboard the rotorcraft is used to capture the positions of the ground vehicles, which are used to define the 3D-path to be followed by the helicopter. Simulation results validating the proposed system are presented and discussed.