This paper proposes a mobile robot motion control and planning system for trajectory tracking and obstacle avoidance in a prior unknown robot environment. The proposed system has two-level control and planning architecture: the higher is used to generate a path, while the lower provides the control actions that drive the robot. The planning level represents a reactive planer which determines on-line way-points during the robot’s movement towards the target and allowing the robot to move autonomously through an environment without colliding with obstacles. The main objective of this algorithm is to reduce the number of obstacles that are taken into consideration when determining the intermediate target point (way-points) in the movement towards the target location. This proposed algorithm is based on the concept of calculating the intersection of the variable target circle and the robot perception circle (VTPC), as well as attractive and repulsive forces. The lower level includes a fuzzy logic controller that drives the robot along generated online trajectory. It compares the current position of the mobile robot with the desired position, generating the appropriate linear speeds for the robot’s wheels to reach the target point in the shortest possible time. A series of simulations demonstrate its effectiveness in generating and executing the paths in various unknown robot environments.

This paper presents a robust control strategy, namely Sliding Mode Control (SMC), to control a two-degree-of-freedom (2DOF) helicopter model. This model is an inherently unstable, nonlinear, and multivariable system with coupling effects between two axes. Due to the mentioned characteristics, helicopter control remains a challenging problem, especially in the presence of external disturbances, parameter perturbation and model uncertainties. In order to overcome some of these difficulties, we developed a second order sliding mode controller based on the super-twisting algorithm (STSMC). It represents a modification of the standard SMC with the aim of improving its control capabilities. The effectiveness and robustness of the used STSMC were verified through simulations and experiments. The results obtained were compared with the same achieved by a previously developed PID and adaptive controllers. The STSMC significantly outperforms the PID controller and additionally improves the efficiency of the used adaptive controller in terms of stability of the system, tracking error, and overall control performance in the presence of system uncertainties and coupling effects between two axes, the azimuth and elevation.

The paper addresses the problem of detecting pedestrians using three dimensional data acquired by an autonomous mobile robot equipped with an on-board 3D laser scanner. Previous works in this field have dealt with various approaches for combining 2D and 3D range data features for the use in pedestrian classification. In this paper we propose an image processing pipeline for generating a depth image from point clouds data and then localizing object candidates from the depth image. It involves the image segmentation, feature extraction and human classification processes within unstructured dynamic environments. Three different approaches for the detection of pedestrians, vehicles and cyclists using only 3D range data were employed as a part of this system. We train and test the classifiers in an open environment, with presence of multiple pedestrians, cyclists and vehicles, using only point cloud data. The effectiveness and robustness of the proposed system are verified through experiments with real data. This system is also capable to deal with a real-time framerate (10Hz) with high accuracy.

The paper deals with the mathematical modeling and control of an unmanned aerial vehicle (UAV), called octocopter, based on a linear quadratic regulator (LQR). The complex multivariable and nonlinear UAV model is linearized and represented in the state space form. Optimal LQR control system, which is composed of combination the altitude (UAV height -${z}$position) and attitude (orientation) controllers, was first designed. Then, this system is extended to provide an additional control of the translation movement in${x}$and${y}$directions. The proposed LQR control structure is capable of controlling the UAV for all position and orientation coordinates while tracking desired 3D trajectory. Simulation studies are performed on the UAV model where the designed LQR controller has been compared with previously developed PD controller.

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 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.

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.

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 deals with the problems of identification and control of nonlinear helicopter model. The helicopter model is achieved by closed loop identification based on grey-box structure through two steps. The first step considers experiment design to minimize the number of parameters to be estimated. It employs a genetic algorithm for parameter value estimation. In the second step, a criteria function was defined for evaluation of model fitness. After that, linearization of nonlinear model was performed and linear state-space model was obtained for use in controller synthesis. The main contribution of this paper is design of loop shaping controller which is capable to control a nonlinear helicopter model. Advantage of this controller is its simpler linear structure and simpler synthesis. The quality of obtained helicopter model and effectiveness of proposed controller are verified both in simulation and experimental modes.

Energy efficiency became more relevant recently. This also includes the construction of energy efficient buildings in terms of heat conservation and dissipation. For analysing the energy efficiency several mapping algorithms are proposed that map indoor environments with added thermal information. Also, several algorithms that generate virtual 3D models are recently presented. One of the main parts of these algoritms are nearest neighbour searching techniques. There are several algorithms that enables the use of nearest neighbour (NN) search. In this paper we present the assessment of R-tree based NN queries in the problem of scalar field mapping that maps a measured temperatures onto reconstructed 3D-mesh of indoor environment. The mesh is reconstructed from the point cloud recorded with 3D laser scanner and thermal imaging camera. We present the performance analysis of the R-tree based NN search with different R-tree types. Also, we present the quality of the scalar field mapping produced with employed R-tree based NN search techniques.

Determining the position of a mobile robot in every time instant from sensor data is the fundamental problem in mobile robotics. This paper considers a localization of holonomous mobile robot solved in this paper using two different approaches: odometry localization and landmark based localization. In both cases the robot is placed in known environment with landmarks whose coordinates were also known. Detecting the landmarks was done by using the Microsoft Kinect camera. For odometry localization four encoders were used. Data acquired from encoders and camera is fused together employing extended Kalman filter in order to get more accurate estimation of position and orientation. Obtained experimental results prove that using encoders without any additional measurements is not enough for getting reliable estimation of robots position. Odometry localization produced an error that accumulates over time, while in the case of landmark based localization, the error is kept inside acceptable limits.

Neural networks have been applied very successfully in the identification and control of nonlinear dynamic systems. The paper presents a design of neural network based control system for 2DOF nonlinear laboratory helicopter model (Humusoft CE 150). The main objective of this paper is to develop artificial neural networks to control helicopter's motors, or consequently elevation and azimuth angles. Neural networks are obtained by cloning various type of controllers designed in our previous papers. Those procedures included a cloning linear PID controller, gain scheduling controller and fuzzy controller.

This paper presents an implementation of soft computing methodologies, like genetic algorithm and fuzzy logic, in identification and control of 2DOF nonlinear helicopter model (Humusoft CE 150). The genetic algorithm is proposed for identification of the physical structure of helicopter system, which contains a helicopter body, main and tail motors and drivers. The quality of helicopter model achieved was validated through simulation and experimental modes. Then, this model is used to elevation and azimuth fuzzy logic Mamdani type controllers design in a simulation mode. The main objective of the paper is to obtain robust and stable controls for wide range of azimuth and elevation angles changing during the long time flight. The robustness and effectiveness of both fuzzy controllers were verified through both simulations and experiments.