The adaptive control system of quadrocopter motion

In this paper we present a system for automatic control of a quadrocopter based on the adaptive control system. The task is to ensure the motion of the quadrocopter along the given route and to control the stabilization of the quadrocopter in the air in a horizontal or in a given angular position by sending control signals to the engines. The nonlinear model of a quadrocopter is expressed in the form of a linear non-stationary system

The adaptive control system of quadrocopter motion

In this paper we present a system for automatic control of a quadrocopter based on the adaptive control system. The task is to ensure the motion of the quadrocopter along the given route and to control the stabilization of the quadrocopter in the air in a horizontal or in a given angular position by sending control signals to the engines. The nonlinear model of a quadrocopter is expressed in the form of a linear non-stationary system

Detection and estimation of moving obstacles for a UAV

In recent years, research interest in Unmanned Aerial Vehicles (UAVs) has been grown rapidly because of their potential use for a wide range of applications. In this paper, we proposed a vision-based detection and position/velocity estimation of moving obstacle for a UAV. The knowledge of a moving obstacle's state, i.e., position, velocity, is essential to achieve better performance for an intelligent UAV system specially in autonomous navigation and landing tasks. The novelties are: (1) the design and implementation of a localization method using sensor fusion methodology which fuses Inertial Measurement Unit (IMU) signals and Pozyx signals; (2) The development of detection and estimation of moving obstacles method based on on-board vision system. Experimental results validate the effectiveness of the proposed approach. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

Mixed marker-based/marker-less visual odometry system for mobile robots

When moving in generic indoor environments, robotic platforms generally rely solely on information provided by onboard sensors to determine their position and orientation. However, the lack of absolute references often leads to the introduction of severe drifts in estimates computed, making autonomous operations really hard to accomplish. This paper proposes a solution to alleviate the impact of the above issues by combining two vision‐based pose estimation techniques working on relative and absolute coordinate systems, respectively. In particular, the unknown ground features in the images that are captured by the vertical camera of a mobile platform are processed by a vision‐based odometry algorithm, which is capable of estimating the relative frame‐to‐frame movements. Then, errors accumulated in the above step are corrected using artificial markers displaced at known positions in the environment. The markers are framed from time to time, which allows the robot to maintain the drifts bounded by additionally providing it with the navigation commands needed for autonomous flight. Accuracy and robustness of the designed technique are demonstrated using an off‐the‐shelf quadrotor via extensive experimental test