This paper presents a nonlinear flatness-based approach for simultaneous control of the active and reactive power of a self-excited induction generator (SEIG) in wind energy conversion system (WECS). The proposed flatness-based controller (FBC) generates the control outputs which are applied to the current-controlled voltage source inverter (CC-VSI) and gearbox. A differential flatness theory is exploited to derive the flat outputs of the SEIG generator as well as to prove that the overall system is differentially flat one. This enables a transformation of this system into the linear canonical (Brunovsky) form and facilitates the design of the controller. The design methodology of the flatness-based controller relies on using a flux-based mathematical model of the SEIG in rotating $dq$ reference frame. The set points of the active and reactive powers are converted into system variables using a high-level reference trajectory generator (HLRTG). The proposed approach provides an efficient decoupled control for both the active and reactive power of the SEIG generator. The efficiency of the proposed control system is confirmed through simulation experiments.

The paper presents a custom-made radiometric thermography system which provides a full radiometric IR inspection. It contains heterogeneous stereo-vision system with RGB and thermal camera, as well as the processor unit with developed embedded software modules. The software units were realized using C++ programming language within ROS development environment. In order to obtain the better representation of points in the space as well as their projections on the cameras planes, the stereo calibration was performed. The correspondence between RGB and thermal images is represented with homography. The object detection is performed using OpenCV feature detectors, while the recognition is carried out by Hu moments computation and K-means clustering. For the showcase, incandescent light bulbs, LED light bulbs and quartz heaters are detected and recognized as such. The developed system is capable of providing a heterogeneous inspection of heating systems, power lines, etc.

The paper deals with the three-dimensional (3D) modelling based on data acquired from 2D laser sensor and IMU (Inertial Measurement Unit) attached to the UAV (Unmanned Aerial Vehicle). The used multi-sensor unit produces 2D scans and provides information about Euler angles or quaternions. These angles are used to describe the UAV orientation in 3D space, more precisely the orientation of the laser sensor. In order to generate 3D occupancy map it is necessary to rotate the laser sensor around its axis yielding the 2D scans being mapped on 3D space using quaternions. Inertial sensor and Sweep LiDAR laser measurements are transmitted to the single board computer Odroid XU4 (SBC). The data fusion was performed under ROS (Robot Operating System) installed on the SBC, producing 3D space points. These points are transmitted over the network to the central computer on which the UAV localization and mapping processes are done within the ROS. The effectiveness of the proposed system for 3D modelling of the UAV environment is verified by experiment.

The paper considers a problem of feature matching and object detection in two images using brute-force matchers. The proposed framework exploited several concurrent algorithms for feature detection and descriptor extraction, such as ORB (Oriented FAST and Rotated BRIEF), BRISK (Binary Robust Invariant Scalable Keypoints), SIFT (Scale Invariant Feature Transform) and SURF (Speeded-Up Robust Features). The feature matching is accomplished by the Brute-Force approach combined with the k-Nearest Neighbors algorithm. The obtained matches are utilized by the robust RANSAC (Random Sample Consensus) method for estimating the transformation between two consecutive images. Therefore, the RANSAC method is employed to improve the outliers removal. The proposed algorithm is designed and implemented using OpenCV library. Its effectiveness and quality are verified through analyses of its execution speed and accuracy of the feature matching.

The paper treats a design of the mobile robot motion framework based on Nonlinear Model Predictive Control (NMPC). This approach relies on laser range-finder measurements and safety regions described around the detected obstacles. The controller optimization involves both actuator and environment constraints excluding safety regions. The safe motion of the mobile robot using the proposed framework is provided in unknown static and dynamic environments. Stability issue of the used closed-loop motion control system is guaranteed employing direct Lyapunov method. The simulation setup was conducted using a 2D simulator (Stage) in the Robot Operating System (ROS) environment. The obtained results through different scenarios demonstrates that proposed NMPC-based framework approach ensures smooth trajectories from start to the goal point.

This paper presents an experimental procedure for the identification of parameters of an octorotor unmanned aerial vehicle (UAV), as well as the obtained model validation via control. The octorotor UAV is a highly nonlinear, multivariable and strongly coupled system. The mathematical model of used UAV includes rigid body dynamics, the Gyroscopic effect and motor dynamics. In order to estimate eleven unknown parameters, the experiments are specially prepared and conducted on the custom made apparatus. Therefore, on basis of obtained measurements, some modifications of the octorotor model are made.

This paper presents a nonlinear flatness-based control (FBC) approach for a full-order doubly fed induction generator (DFIG) in the wind turbine system. Flat outputs of the DFIG and the FBC controller are derived using differential flatness theory. The proposed approach ensures an efficient decoupled control for both active and reactive powers of the DFIG. Also, it provides a smooth trajectory tracking in the start-up and the rest to rest modes without any saturation. Therefore, the system satisfactory operates at a variable speed of the rotor with an effective active/reactive power tracking. The variable rotor speed represents a perturbation caused by changes in the wind speed or different wind energy capacity. The requirements on the active and the reactive power are converted into system variables using a high-level reference trajectory generator (HLRTG). The effectiveness of the proposed system is verified by simulations.

Abstract This paper introduces a novel iterative 3D mapping framework for large scale natural terrain and complex environments. The framework is based on an Iterative-Closest-Point (ICP) algorithm and an iterative error minimization mechanism, allowing robust 3D map registration. This was accomplished by performing pairwise scan registrations without any prior known pose estimation information and taking into account the measurement uncertainties due to the 6D coordinates (translation and rotation) deviations in the acquired scans. Since the ICP algorithm does not guarantee to escape from local minima during the mapping, new algorithms for the local minima estimation and local minima escape process were proposed. The proposed framework is validated using large scale field test data sets. The experimental results were compared with those of standard, generalized and non-linear ICP registration methods and the performance evaluation is presented, showing improved performance of the proposed 3D mapping framework.

Abstract This paper proposes a very effective method for data handling and preparation of the input 3D scans acquired from laser scanner mounted on the Unmanned Ground Vehicle (UGV). The main objectives are to improve and speed up the process of outliers removal for large-scale outdoor environments. This process is necessary in order to filter out the noise and to downsample the input data which will spare computational and memory resources for further processing steps, such as 3D mapping of rough terrain and unstructured environments. It includes the Voxel-subsampling and Fast Cluster Statistical Outlier Removal (FCSOR) subprocesses. The introduced FCSOR represents an extension on the Statistical Outliers Removal (SOR) method which is effective for both homogeneous and heterogeneous point clouds. This method is evaluated on real data obtained in outdoor environment.