Position Control of an Unmanned Aerial Vehicle From a Mobile Ground Vehicle

Quadcopters have been developed with controls providing good maneuverability, simple mechanics, and the ability to hover, take-off and land vertically with precision. Due to their small size, they can get close to targets of interest and furthermore stay undetected at lower heights. The main drawbacks of a quadcopter are its high-power consumption and payload restriction, due to which, the number of onboard sensors is constrained. To overcome this limitation, vision-based localization techniques and remote control for the quadcopter are essential areas of current research. The core objective of this research is to develop a closed loop feedback system between an Unmanned Aerial Vehicle (UAV) and a mobile ground vehicle. With this closed loop system, the moving ground vehicle aims to navigate the UAV remotely. The ground vehicle uses a pure pursuit algorithm to traverse a pre-defined path. A Proportional-Integral-Derivative (PID) controller is actualized for position control and attitude stabilization of the UAV. The issue of tracking and 3D pose-estimation of the UAV in light of vision sensing is explored. An estimator to track the states of the UAV, utilizing the images obtained from a single camera mounted on the ground vehicle is developed. This estimator coupled with a Kalman filter determines the UAV\u27s three dimensional position. The relative position of the UAV with the moving ground vehicle and the control output from a joint centralized PD controller is used to navigate the UAV and follow the motion of the ground vehicle in closed loop to avoid time delays. This closed loop system is simulated in MATLAB and Simulink to validate the proposed control and estimation approach. The results obtained validate the control architecture proposed to attain closed loop feedback between the UAV and the mobile ground vehicle

Learning Pose Estimation for UAV Autonomous Navigation and Landing Using Visual-Inertial Sensor Data

In this work, we propose a robust network-in-the-loop control system for autonomous navigation and landing of an Unmanned-Aerial-Vehicle (UAV). To estimate the UAV’s absolute pose, we develop a deep neural network (DNN) architecture for visual-inertial odometry, which provides a robust alternative to traditional methods. We first evaluate the accuracy of the estimation by comparing the prediction of our model to traditional visual-inertial approaches on the publicly available EuRoC MAV dataset. The results indicate a clear improvement in the accuracy of the pose estimation up to 25% over the baseline. Finally, we integrate the data-driven estimator in the closed-loop flight control system of Airsim, a simulator available as a plugin for Unreal Engine, and we provide simulation results for autonomous navigation and landing

Robust Control and Estimation for Unmanned Aerial Vehicles

In recent years, unmanned aerial vehicles (UAVs) have found applications in many diverse fields encompassing commercial, civil, and military sectors. These applications include surveillance, search and rescue operations, aerial photography, mapping of geographical areas, aerial cargo delivery, to name a few. This research addresses how to develop next-generation UAV systems, namely, effective modeling of UAVs, robust control techniques, and non-linear/robust state estimation. The first part addresses modeling and control of a six-degree-of-freedom unmanned aerial vehicle capable of vertical take-off and landing in the presence of wind disturbances. We design a hybrid vehicle that combines the benefits of both fixed-wing and rotary-wing UAVs. A non-linear model for the hybrid vehicle is built, combining rigid body dynamics, the aerodynamics of the wing, and the dynamics of the motor and propeller. Further, we design an H2 optimal controller to make the UAV robust to wind disturbances. It is easy to achieve robustness in this design framework with respect to wind gusts. The controller is determined by solving a convex optimization problem involving linear matrix inequalities and simulated with a non-linear hybrid UAV model developed in the first section, with a wind gust environment. Further, we compare its results against that of PID and LQR-based control. Our proposed controller results in better performance in terms of root mean squared errors and time responses during two scenarios: hover and level-flight. In the second part of the research, we discuss robust Proportional-Integral-Derivative (PID) control techniques for the quadcopters. PID control is the most commonly used algorithm for designing controllers for unmanned aerial vehicles (UAVs). However, tuning PID gains is a non-trivial task. A number of methods have been developed for tuning PID gains but these methods do not handle wind disturbances, which is a major concern for small UAVs. In this paper, we propose a new method for determining optimized PID gains in the H2 optimal control framework, which achieves improved wind disturbance rejection. The proposed method compares the classical PID control law with the H2 optimal controller to determine the H2 optimal PID gains and involves solving a convex optimization problem. The proposed controller is tested in two scenarios, namely, vertical velocity control, and vertical position control. The results are compared with the existing LQR based PID tuning method. A good performance of the controller requires an accurate estimation of states from noisy measurements. Therefore, the third part of the research concentrates on the accurate attitude estimation of UAVs. Most UAV systems use a combination of a gyroscope, an accelerometer, and a magnetometer to obtain measurements and estimate attitude. Under this paradigm of sensor fusion, the Extended Kalman Filter (EKF) is the most popular algorithm for attitude estimation in UAVs. In this work, we propose a novel estimation technique called extended H2 filter that can overcome the limitations of the EKF, specifically with respect to computational speed, memory usage, and root mean squared error. We formulate our attitude-estimation algorithm using two distinct coordinate representations for the vehicle's orientation: Euler angles and unit quaternions, each with its own sets of benefits and challenges. The H2 optimal filter gain is designed offline about a nominal operating point by solving a convex optimization problem, and the filter dynamics is implemented using the nonlinear system dynamics. This implementation of this H2 optimal estimator is referred as the extended H2 estimator. The proposed technique is tested on four cases corresponding to long time-scale motion, fast time-scale motion, transition from hover to forward flight for VTOL aircrafts and an entire flight cycle (from take-off to landing). Its results are compared against that of the EKF in terms of the aforementioned performance metrics

A virtual odometer for a Quadrotor Micro Aerial Vehicle

This paper describes the synthesis and evaluation of a "virtual odometer" for a Quadrotor Micro Aerial Vehicle. Availability of a velocity estimate has the potential to enhance the accuracy of mapping, estimation and control algorithms used with quadrotors, increasing the effectiveness of their applications. As a result of the unique dynamic characteristics of the quadrotor, a dual axis accelerometer mounted parallel to the propeller plane provides measurements that are directly proportional to vehicle velocities in that plane. Exploiting this insight, we encapsulate quadrotor dynamic equations which relate acceleration, attitude and the aerodynamic propeller drag in an extended Kalman filter framework for the purpose of state estimation. The result is a drift free estimation of lateral and longitudinal components of translational velocity and roll and pitch components of attitude of the quadrotor. Real world data sets gathered from two different quadrotor platforms, together with ground truth data from a Vicon system, are used to evaluate the effectiveness of the proposed algorithm and demonstrate that drift free estimates for the velocity and attitude can be obtained