Autonomous landing of a quadrotor on a moving platform using vision-based FOFPID control

This research deals with the autonomous landing maneuver of a quadrotor unmanned aerial vehicle (UAV) on an unmanned ground vehicle (UGV). It is assumed that the UGV moves independently, and there is no communication and collaboration between the two vehicles. This paper aims at the design of a closed-loop vision-based control system for quadrotor UAV to perform autonomous landing maneuvers in the possible minimum time despite the wind-induced disturbance force. In this way, a fractional-order fuzzy proportional-integral-derivative controller is introduced for the nonlinear under-actuated system of a quadrotor. Also, a feedback linearization term is included in the control law to compensate model nonlinearities. A supervisory control algorithm is proposed as an autonomous landing path generator to perform fast, smooth, and accurate landings. On the other hand, a compound AprilTag fiducial marker is employed as the target of a vision positioning system, enabling high precision relative positioning in the range between 10 and 350 cm height. A software-in-the-loop simulation testbed is realized on the windows platform. Numerical simulations with the proposed control system are carried out, while the quadrotor system is exposed to different disturbance conditions and actuator dynamics with saturated thrust output are considered.

Research on Body Positioning Measurement Method of Bolting Robot based on Vision Theory

In view of the intelligent demand of underground roadway support and the precise positioning of underground unmanned fully mechanized face, a method of body positioning measurement of bolting robot based on the principle of monocular vision is proposed. In this paper, a vehicle body positioning model based on image data is established. The data is obtained by camera, and the transformation between image coordinates and world coordinates is completed by coordinate system transformation. The monocular vision positioning system of bolting robot is designed, and the simulation experimental model is built to measure the effective positioning distance of monocular vision positioning system in the simulation experimental conditions. The experimental platform of bolting robot is designed, and the vehicle is measured Real time data of body positioning, analysis of experimental error and demonstration of reliability of the method. In this method, the real-time localization of underground mine is realized by the robot of bolting, and the accuracy and efficiency of localization are improved, which lays the foundation for the localization control of mining face and the automation and unmanned of the robot of bolting.

Vision Positioning-Based Estimation Method and Its Simulation Studies on State of Underwater Manipulator

Work class remote operated vehicles (ROVs) are generally equipped with underwater manipulators and are widely used in underwater intervention and maintenance tasks. As the load of underwater operation is relatively heavy, most commercial underwater manipulators are hydraulically actuated and are not equipped with any sensor for joint angles to keep their architectures compact. Therefore, the automatic control methods widely used in industrial robots cannot be simply applied to underwater manipulators. In this paper, an estimation method on joint angles of manipulator is presented, in which several markers are arranged on the arm links and positioned from the corresponding cameras; consequently, the joint angles of the manipulator are estimated. The simulation results show that under typical optical vision positioning error (RMS: 5 mm), the positioning error of the end effector can be estimated as about 10 mm (RMS), which means that the proposed estimation method is feasible for the state estimation for automatic control of underwater manipulators.