This paper presents the Quaternion-based Robust Adaptive Unscented Kalman Filter (QRAUKF) for attitude estimation. The proposed methodology modifies and extends the standard UKF equations to consistently accommodate the non-Euclidean algebra of unit quaternions and to add robustness to fast and slow variations in the measurement uncertainty. To deal with slow time-varying perturbations in the sensors, an adaptive strategy based on covariance matching that tunes the measurement covariance matrix online is used. Additionally, an outlier detector algorithm is adopted to identify abrupt changes in the UKF innovation, thus rejecting fast perturbations. Adaptation and outlier detection make the proposed algorithm robust to fast and slow perturbations such as external magnetic field interference and linear accelerations. Comparative experimental results that use an industrial manipulator robot as ground truth suggest that our method overcomes a trusted commercial solution and other widely used open source algorithms found in the literature.

Autonomous navigation of unmanned vehicles in forests is a challenging task. In such environments, due to the canopies of the trees, information from Global Navigation Satellite Systems (GNSS) can be degraded or even unavailable. Also, because of the large number of obstacles, a previous detailed map of the environment is not practical. In this paper, we solve the complete navigation problem of an aerial robot in a sparse forest, where there is enough space for the flight and the GNSS signals can be sporadically detected. For localization, we propose a state estimator that merges information from GNSS, Attitude and Heading Reference Systems (AHRS), and odometry based on Light Detection and Ranging (LiDAR) sensors. In our LiDAR-based odometry solution, the trunks of the trees are used in a feature-based scan matching algorithm to estimate the relative movement of the vehicle. Our method employs a robust adaptive fusion algorithm based on the unscented Kalman filter. For motion control, we adopt a strategy that integrates a vector field, used to impose the main direction of the movement for the robot, with an optimal probabilistic planner, which is responsible for obstacle avoidance. Experiments with a quadrotor equipped with a planar LiDAR in an actual forest environment is used to illustrate the effectiveness of our approach.

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Hydroelectric power plants need to ensure an uninterrupted energy supply. To achieve this objective, activities of maintenance and inspection are done periodically. The use of divers to do these maintenance in submerged areas, implied high risks for them. In this sense, the automated inspection of such facilities through underwater vehicles can be a satisfactory solution. This article presents a comparison between two control techniques applied to underwater vehicle, Feedback Linearization and Sliding Mode.These strategies are responsible to ensure the following two requirements. First, to make the vehicle overcome the velocity gradient caused by dam's water intakes. Second, to follow the desired trajectory. The control laws were tested by analysing their performance in a helical reference trajectory with the presence of disturbance caused by water intakes.

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In this paper, we present a methodology to make an autonomous quadrotor fly through a sequence of gates only with on-board sensors. Our work is a solution to the AlphaPilot Challenge, proposed by the Lookheed Martin Company. In the challenge, the quadriprotor must be able to compete with human-piloted drones in a race. First, we propose a strategy to generate a smooth trajectory that passes through the gates. Then we developed a localization system, which merges image data from an on-board camera with IMU data. Finally, we present an artificial vector field based strategy used to control the quadrotor. Our results are validated with simulations in the official simulator of the competition. Resumo: Este artigo apresenta uma metodologia para guiar um quadrirrotor autônomo através de uma sequência de portões utilizando apenas sensores onboard. O trabalhoé uma solução para o desafio AlphaPilot, proposto pela companhia Lookheed Martin. No desafio, o quadrirrotor deve ser capaz de competir com drones pilotados por humanos em uma corrida. Primeiramente, uma estratégia para gerar uma trajetória suave que trafega através das janelasé proposta. Então, e desenvolvido um sistema de localização que une informações de imagens de uma câmera frontal com informações da IMU. Finalmente, uma estratégia baseada em campos vetoriais artificiais utilizada para controlar o quadrirrotoré apresentada. Os resultados são validados com simulações no simulador oficial da competição.