Quadrotor Micro Aerial Vehicle Research Articles

Toward Developing an Indoor Localization System for MAVs Using Two or Three RF Range Anchors: An Observability Based Approach

This study performs a nonlinear observability analysis on radio frequency (RF) range assisted inertial navigation system (INS) for localizing quadrotor micro-aerial vehicles (MAV) in indoor environments. The objective is to use fewer number of RF range nodes as possible to support the efficient scalability of the localization system. The proposed INS formulation incorporates the effect of aerodynamic drag forces, which allows this novel INS to operate without having to use a velocity sensor. The nonlinear observability analysis is carried out for two distinct cases where the range is measured between the MAV and RF anchors placed at known locations. The first case uses three anchors, and for the second case, the analysis is repeated for two range anchors. For each case, different scenarios are considered to identify unobservable conditions of the proposed INS, and the corresponding unobservable modes for those scenarios are determined. These unobservable modes are validated through numerical simulation. The analysis facilitates the range assisted localization of MAVs when there are less than the typical four range configuration and allows planning of the trajectory of the MAV while preserving the observability of the INS. The main contributions of this paper are as follows: 1) nonlinear observability analysis of a range assisted INS for quadrotor MAV with three or two range measurements, 2) theoretical derivation of unobservable trajectories and corresponding unobservable modes, 3) numerical validation of the unobservable modes and experimental validation of the proposed INS performance.

Adaptive Trajectory Tracking for Quadrotor MAVs in Presence of Parameter Uncertainties and External Disturbances

This paper presents an adaptive trajectory tracking control strategy for quadrotor micro aerial vehicles (MAVs). The proposed approach, while maintaining the common assumption of an orientation dynamics faster than the translational one, removes the assumption of absence of external disturbances and of geometric center coincident with the Center of Mass (CoM). In particular, the trajectory tracking control law is made adaptive with respect to the presence of external forces and moments (e.g., due to wind) and to the uncertainty of parameters of the dynamic model, such as the position of the CoM. A stability analysis is presented to analytically support the proposed controller, while numerical simulations are provided in order to validate its performance.

Invariant Observer-Based State Estimation for Micro-Aerial Vehicles in GPS-Denied Indoor Environments Using an RGB-D Camera and MEMS Inertial Sensors

This paper presents a non-linear state observer-based integrated navigation scheme for estimating the attitude, position and velocity of micro aerial vehicles (MAV) operating in GPS-denied indoor environments, using the measurements from low-cost MEMS (micro electro-mechanical systems) inertial sensors and an RGB-D camera. A robust RGB-D visual odometry (VO) approach was developed to estimate the MAV’s relative motion by extracting and matching features captured by the RGB-D camera from the environment. The state observer of the RGB-D visual-aided inertial navigation was then designed based on the invariant observer theory for systems possessing symmetries. The motion estimates from the RGB-D VO were fused with inertial and magnetic measurements from the onboard MEMS sensors via the state observer, providing the MAV with accurate estimates of its full six degree-of-freedom states. Implementations on a quadrotor MAV and indoor flight test results demonstrate that the resulting state observer is effective in estimating the MAV’s states without relying on external navigation aids such as GPS. The properties of computational efficiency and simplicity in gain tuning make the proposed invariant observer-based navigation scheme appealing for actual MAV applications in indoor environments.

Improved State Estimation in Quadrotor MAVs: A Novel Drift-Free Velocity Estimator

quadrotor microaerial vehicles (MAVs) are simple robotic platforms with regard to their construction. In their basic form, they are no more than two counterrotating propeller pairs attached symmetrically to a rigid crosslike frame, along with the means to control the speed of each individual propeller. This symmetric design has enabled the quadrotor to become a simple but powerful vertical takeoff and landing aerial platform popular among the robotics community.

四旋翼微型飞行器的区间二型

The adaptive control scheme of a quadrotor Micro Aerial Vehicle(MAV) by using Interval Type-II Fuzzy Neural Network(IT_IIFNN) was proposed to improve the control accuracy that was declined by the uncertainty,external disturbances,etc.Based on the quadrotor MAV dynamic modeling,an adaptive controller composing of two parts was designed by using the IT_IIFNN,in which the IT_IIFNN was developed to approximate the uncertainty function and a robust compensator was proposed to confront the approximate errors of IT_IIFNN and external disturbances in real-time.Moreover,the Lyapunor stability theory was taken to prove the stability of the closed-loop control system in the quadrotor MAV.Finally,the superiority of the adaptive controller was verified by a prototype of the quadrotor MAV,which is shown that the tracking error approximated is 10-2 under the interference conditions of wind speed of 1.5 m/s.Experiments demonstrate that proposed control scheme can offer perfect tracking accuracy,stability and robustness.

Robust control of quadrotor MAV using self‐organizing interval type‐II fuzzy neural networks (SOIT‐IIFNNs) controller

PurposeQuadrotor micro aerial vehicle (MAV) is nonlinear and under actuated plant, and it is difficult to obtain an accurate mathematical model for quadrotor MAV due to uncertainties. The purpose of this paper is to propose one robust control strategy for quadrotor MAV to accommodate system uncertainties, variations, and external disturbances.Design/methodology/approachThe robust control strategy is composed of two self‐organizing interval type‐II fuzzy neural networks (SOIT‐IIFNNs) and one PD controller: the PD controller is adopted to control the attitude and position; one of the SOIT‐IIFNNs is designed to learn the inverse model of quadrotor MAV online; the other SOIT‐IIFNNs is the copy of the former one to compensate for model errors, system uncertainties and external disturbances, both structure and parameters of SOIT‐IIFNNs are tuned online at the same time, and then the stability of the resulting quadrotor MAV closed‐loop control system is proved using Lyapunov stability theory.FindingsThe validity of the proposed control method has been verified through real‐time experiments. The experimental results show that the performance of SOIT‐IIFNNs is significantly improved compared with Backstepping‐based controller.Practical implicationsThis approach has been used in quadrotor MAV, the controller works well, and it could guarantee quadrotor MAV control system with good performances under uncertainties, variations, and external disturbances.Originality/valueThe proposed SOIT‐IIFNNs controller is interesting for the design of an intelligent control scheme. The main contributions of this paper are: the overall closed‐loop control system is globally stable, demonstrated by Lyapunov stable theory; the tracking error can be asymptotically attenuated to a desired small level around zero by appropriate chosen parameters and learning rates; and the quadrotor MAV control system based on SOIT‐IIFNNs controller can achieve favorable tracking performance.

Autonomous Hovering and Landing of a Quad-rotor Micro Aerial Vehicle by Means of on Ground Stereo Vision System

On ground stereo vision system is used for autonomous hovering and landing of a quadrotor Micro Aerial Vehicle (MAV). This kind of system has an advantage to support embedded vision system for autonomous hovering and landing, since an embedded vision system occasionally gives inaccurate distance calculation due to either vibration problem or unknown geometry of the landing target. Color based object tracking by using Continuously Adaptive Mean Shift (CAMSHIFT) algorithm was examined. Nonlinear model of quad-rotor MAV and a PID controller were used for autonomous hovering and landing. The result shows that the Camshift based object tracking algorithm has good performance. Additionally, the comparison between the stereo vision system based and GPS based autonomous hovering of a quad-rotor MAV shows that stereo vision system has better performance. The accuracy of the stereo vision system is about 1 meter in the longitudinal and lateral direction when the quad-rotor flies in 6 meters of altitude. In the same experimental condition, the GPS based system accuracy is about 3 meters. Additionally, experiment on autonomous landing gives a reliable result.

1A2-C23 Sliding Mode Controller for Vision Based Autonomous Hovering of Quad-rotor MAV

We investigate the possibility of a sliding mode controller (SMC) for autonomous hovering of a quad-rotor Micro Aerial Vehicle (MAV) based on an on ground stereo vision system. The object tracking algorithm used here is running average background subtraction. Among the background subtraction algorithms for object tracking, running average is known to have the fastest processing speed and the lowest memory requirement. Stereo vision system is known to have a good performance in measuring the distance from camera to object without any information about the object geometry in advance. SMC is known to have advantage of insensitivity to the model errors, parametric uncertainties and other disturbances. The experiment on autonomous hovering by using running average method for object tracking and SMC for the flight control shows a reliable result.