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 full degrees-of-freedom (DOFs) robust control design for a nonlinear quadrotor unmanned aerial vehicle (UAV) operating under bounded disturbances. Second-order sliding modes controllers (SOSMCs) are designed so that the quadrotor UAV can follow a 3D trajectory in the presence of model uncertainties, underactuation, as well as external disturbances that may be matched or mismatched, and vanishing or nonvanishing. The stability analysis of the closed-loop system is presented via the Lyapunov method, showing the finite-time convergence of the system trajectories to the sliding surfaces, as well as the finite-time convergence of the quadrotor position and attitude to their reference values. The high-gain adaptation (HGA) method is adopted in the SOSMC technique, called SOSMC-HGA, to alleviate the chattering phenomenon. Simulation studies in different scenarios demonstrate that the SOSMC technique exhibits superior tracking performance and robustness properties compared to concurrent control methods for tracking reference trajectories of quadrotor UAVs. The simulation results confirm that SOSMC-HGA significantly attenuates the chattering phenomenon in control signals and system states, which is an important improvement, as it increases the safety of UAVs and reduces power consumption.

Control design for multi-rotor aerial vehicles (MAVs) is quite challenging problem due to their nonlinearitles, unknown dynamics, parametric uncertainties, an underactuated property, a nonlinear coupling dynamics and external disturbances. This paper introduces a first order sliding mode control (FOSMC) for robust stabilization of an under-actuated quad-rotor unmanned aerial vehicle (UAV) operating in the presence of external disturbances. The proposed FOSMC guarantees a finite time convergence of the system trajectories to the sliding surface. Obtained simulations show that the FOSM based approach improves robustness properties compared with the concurrent techniques, and enhance tracking performance of the quad-rotor UAV exposed to external disturbances.

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.

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 detailed octorotor model derivation. The full derivation of the rigid octorotor body dynamics based on the Newton-Euler approach including the Gyroscopic effect and motor dynamics is given. We also discuss a generalization of the model thus making it applicable to any symmetric and balanced multirotor aerial vehicle (MAV) system with even number of rotors. Finally, simple stabilization control is designed and compared with the state of the art results.

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.

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.