Contributions to automated realtime underwater navigation

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2012This dissertation presents three separate–but related–contributions to the art of underwater navigation. These methods may be used in postprocessing with a human in the loop, but the overarching goal is to enhance vehicle autonomy, so the emphasis is on automated approaches that can be used in realtime. The three research threads are: i) in situ navigation sensor alignment, ii) dead reckoning through the water column, and iii) model-driven delayed measurement fusion. Contributions to each of these areas have been demonstrated in simulation, with laboratory data, or in the field–some have been demonstrated in all three arenas. The solution to the in situ navigation sensor alignment problem is an asymptotically stable adaptive identifier formulated using rotors in Geometric Algebra. This identifier is applied to precisely estimate the unknown alignment between a gyrocompass and Doppler velocity log, with the goal of improving realtime dead reckoning navigation. Laboratory and field results show the identifier performs comparably to previously reported methods using rotation matrices, providing an alignment estimate that reduces the position residuals between dead reckoning and an external acoustic positioning system. The Geometric Algebra formulation also encourages a straightforward interpretation of the identifier as a proportional feedback regulator on the observable output error. Future applications of the identifier may include alignment between inertial, visual, and acoustic sensors. The ability to link the Global Positioning System at the surface to precision dead reckoning near the seafloor might enable new kinds of missions for autonomous underwater vehicles. This research introduces a method for dead reckoning through the water column using water current profile data collected by an onboard acoustic Doppler current profiler. Overlapping relative current profiles provide information to simultaneously estimate the vehicle velocity and local ocean current–the vehicle velocity is then integrated to estimate position. The method is applied to field data using online bin average, weighted least squares, and recursive least squares implementations. This demonstrates an autonomous navigation link between the surface and the seafloor without any dependence on a ship or external acoustic tracking systems. Finally, in many state estimation applications, delayed measurements present an interesting challenge. Underwater navigation is a particularly compelling case because of the relatively long delays inherent in all available position measurements. This research develops a flexible, model-driven approach to delayed measurement fusion in realtime Kalman filters. Using a priori estimates of delayed measurements as augmented states minimizes the computational cost of the delay treatment. Managing the augmented states with time-varying conditional process and measurement models ensures the approach works within the proven Kalman filter framework–without altering the filter structure or requiring any ad-hoc adjustments. The end result is a mathematically principled treatment of the delay that leads to more consistent estimates with lower error and uncertainty. Field results from dead reckoning aided by acoustic positioning systems demonstrate the applicability of this approach to real-world problems in underwater navigation.I have been financially supported by: the National Defense Science and Engineering Graduate (NDSEG) Fellowship administered by the American Society for Engineering Education, the Edwin A. Link Foundation Ocean Engineering and Instrumentation Fellowship, and WHOI Academic Programs office

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

Cooperative lateral vehicle guidance control for automated vehicles with Steer-by-Wire systems

With the global trend towards automated driving, fault-tolerant onboard power supply systems are introduced into modern vehicles and the level of driving automation is continuously increasing. These advancements contribute to the applicability of Steer-by-Wire systems and the development of automated lateral vehicle guidance control functions. For the market acceptance of automated driving, the lateral vehicle guidance control function must hereby be cooperative, that is it must accept driver interventions. Existing approaches for automated lateral vehicle guidance commonly do not consider driver interventions. If unconsidered in the control loop, the driver intervention is interpreted as an external disturbance that is actively compensated by feedback. This thesis addresses the development of a cooperative lateral vehicle guidance control concept, which enables a true coexistence between manual steering control by the driver and automated steering control. To this end, the subordinate controls of the Steer-by-Wire system for the manual and automated driving mode are initially presented. These include the steering feel generation and steering torque control of the Steer-by-Wire Handwheel Actuator for the manual driving mode, which is structurally extended to a cascade steering position control for the automated driving mode. Subsequently, a superposition control is introduced, which fuses steering torque and position control. The resulting cooperative Handwheel Actuator control achieves precise tracking of the reference steering position in automated driving mode but accepts driver interventions. Thus, the driver can override the active control and experiences a natural steering feel. The transitions hereby are seamless as no blending, gain scheduling or controller output saturation is required. Subsequently, the superimposed lateral vehicle guidance controller for the automated driving mode is described, which computes the reference steering position for the respective Steer-by-Wire controls. In contrast to existing approaches, the plant model equations are rearranged to isolate the vehicle speed dependent dynamics. Thereafter, the concept of inverse nonlinearity control is employed, using a virtual control loop and feedback linearization for an online inversion of the nonlinear plant dynamics. The remaining plant is fully linear and independent of vehicle speed. Consequently, one controller can be synthesized that is valid for all vehicle speeds. The closed and open loop system thereby have the same dynamics independent of vehicle speed, which significantly simplifies control synthesis, analysis, and performance tuning in the vehicle. For considering the future reference path information and constraints on the maximum steering position within the control law, a linear Model Predictive Controller synthesis is selected. The combination of inverse nonlinearity control and linear Model Predictive Controller thus results in a Nonlinear Adaptive Model Predictive Control concept, which makes commonly applied gain scheduling fully obsolete. The controller is structurally extended by a cooperative dynamic feedforward control for considering driver interventions within the control loop. Consequently, the driver can override the active control and seamlessly modify the lateral vehicle motion. A variety of nonlinear simulation analyses and real vehicle tests demonstrate the effectiveness of the proposed control concept

PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision

We describe a novel quadrotor Micro Air Vehicle (MAV) system that is designed to use computer vision algorithms within the flight control loop. The main contribution is a MAV system that is able to run both the vision-based flight control and stereo-vision-based obstacle detection parallelly on an embedded computer onboard the MAV. The system design features the integration of a powerful onboard computer and the synchronization of IMU-Vision measurements by hardware timestamping which allows tight integration of IMU measurements into the computer vision pipeline. We evaluate the accuracy of marker-based visual pose estimation for flight control and demonstrate marker-based autonomous flight including obstacle detection using stereo vision. We also show the benefits of our IMU-Vision synchronization for egomotion estimation in additional experiments where we use the synchronized measurements for pose estimation using the 2pt+gravity formulation of the PnP proble

Control design for UAV quadrotors via embedded model control

In this paper, a control system for unmanned aerial vehicles (UAVs) is designed, tested in simulation by means of a high-fidelity simulator, and then applied to a real quadrotor UAV. A novel approach is proposed for the control design, based on the combination of two methodologies: feedback linearization (FL) and embedded model control (EMC). FL allows us to properly transform the UAV dynamics into a form suitable for EMC; EMC is then used to control the transformed system. A key feature of EMC is that it encompasses a so-called extended state observer (ESO), which not only recovers the system state but also gives a real-time estimate of all the disturbances/uncertainties affecting the system. This estimate is used by the FL-EMC control law to reject the aforementioned disturbances/uncertainties, including those collected via the FL, allowing a robustness and performance enhancement. This approach allows us to combine FL and EMC strengths. Most notably, the entire process is made systematic and application oriented. To set-up a reliable UAV attitude observer, an effective attitude sensors fusion is proposed and also benchmarked with an enhanced complementary filter. Finally, to enhance the closed-loop performance, a complete tuning procedure, encompassing frequency requirements, is outlined, based on suitably defined stability and performance metrics

confined spaces industrial inspection with micro aerial vehicles and laser range finder localization

This work addresses the problem of semi-automatic inspection and navigation in confined environments. A system that overcomes many challenges at the state of the art is presented. It comprises a mu..

The MRS UAV System: Pushing the Frontiers of Reproducible Research, Real-world Deployment, and Education with Autonomous Unmanned Aerial Vehicles

We present a multirotor Unmanned Aerial Vehicle control (UAV) and estimation system for supporting replicable research through realistic simulations and real-world experiments. We propose a unique multi-frame localization paradigm for estimating the states of a UAV in various frames of reference using multiple sensors simultaneously. The system enables complex missions in GNSS and GNSS-denied environments, including outdoor-indoor transitions and the execution of redundant estimators for backing up unreliable localization sources. Two feedback control designs are presented: one for precise and aggressive maneuvers, and the other for stable and smooth flight with a noisy state estimate. The proposed control and estimation pipeline are constructed without using the Euler/Tait-Bryan angle representation of orientation in 3D. Instead, we rely on rotation matrices and a novel heading-based convention to represent the one free rotational degree-of-freedom in 3D of a standard multirotor helicopter. We provide an actively maintained and well-documented open-source implementation, including realistic simulation of UAV, sensors, and localization systems. The proposed system is the product of years of applied research on multi-robot systems, aerial swarms, aerial manipulation, motion planning, and remote sensing. All our results have been supported by real-world system deployment that shaped the system into the form presented here. In addition, the system was utilized during the participation of our team from the CTU in Prague in the prestigious MBZIRC 2017 and 2020 robotics competitions, and also in the DARPA SubT challenge. Each time, our team was able to secure top places among the best competitors from all over the world. On each occasion, the challenges has motivated the team to improve the system and to gain a great amount of high-quality experience within tight deadlines.Comment: 28 pages, 20 figures, submitted to Journal of Intelligent & Robotic Systems (JINT), for the provided open-source software see http://github.com/ctu-mr