Model-Based Control of Flying Robots for Robust Interaction under Wind Influence

Model-Based Control of Flying Robots for Robust Interaction under Wind Influence The main goal of this thesis is to bridge the gap between trajectory tracking and interaction control for flying robots in order to allow physical interaction under wind influence by making aerial robots aware of the disturbance, interaction, and faults acting on them. This is accomplished by reasoning about the external wrench (force and torque) acting on the robot, and discriminating (distinguishing) between wind, interactions, and collisions. This poses the following research questions. First, is discrimination between the external wrench components even possible in a continuous real-time fashion for control purposes? Second, given the individual wrench components, what are effective control schemes for interaction and trajectory tracking control under wind influence? Third, how can unexpected faults, such as collisions with the environment, be detected and handled efficiently and effectively? In the interest of the first question, a fourth can be posed: is it possible to obtain a measurement of the wind speed that is independent of the external wrench? In this thesis, model-based methods are applied in the pursuit of answers to these questions. This requires a good dynamics model of the robot, as well as accurately identified parameters. Therefore, a systematic parameter identification procedure for aerial robots is developed and applied. Furthermore, external wrench estimation techniques from the field of robot manipulators are extended to be suitable for aerial robots without the need of velocity measurements, which are difficult to obtain in this context. Based on the external wrench estimate, interaction control techniques (impedance and admittance control) are extended and applied to flying robots, and a thorough stability proof is provided. Similarly, the wrench estimate is applied in a geometric trajectory tracking controller to compensate external disturbances, to provide zero steady-state error under wind influence without the need of integral control action. The controllers are finally combined into a novel compensated impedance controller, to facilitate the main goal of the thesis. Collision detection is applied to flying robots, providing a low level reflex reaction that increases safety of these autonomous robots. In order to identify aerodynamic models for wind speed estimation, flight experiments in a three-dimensional wind tunnel were performed using a custom-built hexacopter. This data is used to investigate wind speed estimation using different data-driven aerodynamic models. It is shown that good performance can be obtained using relatively simple linear regression models. In this context, the propeller aerodynamic power model is used to obtain information about wind speed from available motor power measurements. Leveraging the wind tunnel data, it is shown that power can be used to obtain the wind speed. Furthermore, a novel optimization-based method that leverages the propeller aerodynamics model is developed to estimate the wind speed. Essentially, these two methods use the propellers as wind speed sensors, thereby providing an additional measurement independent of the external force. Finally, the novel topic of simultaneously discriminating between aerodynamic, interaction, and fault wrenches is opened up. This enables the implementation of novel types of controllers that are e.g. compliant to physical interaction, while compensating wind disturbances at the same time. The previously unexplored force discrimination topic has the potential to even open a new research avenue for flying robots

Deep-Sea Model-Aided Navigation Accuracy for Autonomous Underwater Vehicles Using Online Calibrated Dynamic Models

In this work, the accuracy of inertial-based navigation systems for autonomous underwater vehicles (AUVs) in typical mapping and exploration missions up to 5000m depth is examined. The benefit of using an additional AUV motion model in the navigation is surveyed. Underwater navigation requires acoustic positioning sensors. In this work, so-called Ultra-Short-Baseline (USBL) devices were used allowing the AUV to localize itself relative to an opposite device attached to a (surface) vehicle. Despite their easy use, the devices\u27 absolute positioning accuracy decreases proportional to range. This makes underwater navigation a sophisticated estimation task requiring integration of multiple sensors for inertial, orientation, velocity and position measurements. First, error models for the necessary sensors are derived. The emphasis is on the USBL devices due to their key role in navigation - besides a velocity sensor based on the Doppler effect. The USBL model is based on theoretical considerations and conclusions from experimental data. The error models and the navigation algorithms are evaluated on real-world data collected during field experiments in shallow sea. The results of this evaluation are used to parametrize an AUV motion model. Usually, such a model is used only for model-based motion control and planning. In this work, however, besides serving as a simulation reference model, it is used as a tool to improve navigation accuracy by providing virtual measurements to the navigation algorithm (model-aided navigation). The benefit of model-aided navigation is evaluated through Monte Carlo simulation in a deep-sea exploration mission. The final and main contributions of this work are twofold. First, the basic expected navigation accuracy for a typical deep-sea mission with USBL and an ensemble of high-quality navigation sensors is evaluated. Secondly, the same setting is examined using model-aided navigation. The model-aiding is activated after the AUV gets close to sea-bottom. This reflects the case where the motion model is identified online which is only feasible if the velocity sensor is close to the ground (e.g. 100m or closer). The results indicate that, ideally, deep-sea navigation via USBL can be achieved with an accuracy in range of 3-15m w.r.t. the expected root-mean-square error. This also depends on the reference vehicle\u27s position at the surface. In case the actual estimation certainty is already below a certain threshold (ca. <4m), the simulations reveal that the model-aided scheme can improve the navigation accuracy w.r.t. position by 3-12%

Virtual Model Building for Multi-Axis Machine Tools Using Field Data

Accurate machine dynamic models are the foundation of many advanced machining technologies such as virtual process planning and machine condition monitoring. Viewing recent designs of modern high-performance machine tools, to enhance the machine versatility and productivity, the machine axis configuration is becoming more complex and diversified, and direct drive motors are more commonly used. Due to the above trends, coupled and nonlinear multibody dynamics in machine tools are gaining more attention. Also, vibration due to limited structural rigidity is an important issue that must be considered simultaneously. Hence, this research aims at building high-fidelity machine dynamic models that are capable of predicting the dynamic responses, such as the tracking error and motor current signals, considering a wide range of dynamic effects such as structural flexibility, inter-axis coupling, and posture-dependency. Building machine dynamic models via conventional bottom-up approaches may require extensive investigation on every single component. Such approaches are time-consuming or sometimes infeasible for the machine end-users. Alternatively, as the recent trend of Industry 4.0, utilizing data via Computer Numerical Controls (CNCs) and/or non-intrusive sensors to build the machine model is rather favorable for industrial implementation. Thus, the methods proposed in this thesis are top-down model building approaches, utilizing available data from CNCs and/or other auxiliary sensors. The achieved contributions and results of this thesis are summarized below. As the first contribution, a new modeling and identification technique targeting a closed-loop control system of coupled rigid multi-axis feed drives has been developed. A multi-axis closed-loop control system, including the controller and the electromechanical plant, is described by a multiple-input multiple-output (MIMO) linear time-invariant (LTI) system, coupled with a generalized disturbance input that represents all the nonlinear dynamics. Then, the parameters of the open-loop and closed-loop dynamic models are respectively identified by a strategy that combines linear Least Squares (LS) and constrained global optimization. This strategy strikes a balance between model accuracy and computational efficiency. This proposed method was validated using an industrial 5-axis laser drilling machine and an experimental feed drive, achieving 2.38% and 5.26% root mean square (RMS) prediction error, respectively. Inter-axis coupling effects, i.e., the motion of one axis causing the dynamic responses of another axis, are correctly predicted. Also, the tracking error induced by motor ripple and nonlinear friction is correctly predicted as well. As the second contribution, the above proposed methodology is extended to also consider structural flexibility, which is a crucial behavior of large-sized industrial 5-axis machine tools. More importantly, structural vibration is nonlinear and posture-dependent due to the nature of a multibody system. In this thesis, prominent cases of flexibility-induced vibrations in a linear feed drive are studied and modeled by lumped mass-spring-damper system. Then, a flexible linear drive coupled with a rotary drive is systematically analyzed. It is found that the case with internal structural vibration between the linear and rotary drives requires an additional motion sensor for the proposed model identification method. This particular case is studied with an experimental setup. The thesis presents a method to reconstruct such missing internal structural vibration using the data from the embedded encoders as well as a low-cost micro-electromechanical system (MEMS) inertial measurement unit (IMU) mounted on the machine table. It is achieved by first synchronizing the data, aligning inertial frames, and calibrating mounting misalignments. Finally, the unknown internal vibration is reconstructed by comparing the rigid and flexible machine kinematic models. Due to the measurement limitation of MEMS IMUs and geometric assembly error, the reconstructed angle is unfortunately inaccurate. Nevertheless, the vibratory angular velocity and acceleration are consistently reconstructed, which is sufficient for the identification with reasonable model simplification. Finally, the reconstructed internal vibration along with the gathered servo data are used to identify the proposed machine dynamic model. Due to the separation of linear and nonlinear dynamics, the vibratory dynamics can be simply considered by adding complex pole pairs into the MIMO LTI system. Experimental validation shows that the identified model is able to predict the dynamic responses of the tracking error and motor force/torque to the input command trajectory and external disturbances, with 2% ~ 6% RMS error. Especially, the vibratory inter-axis coupling effect and posture-dependent effect are accurately depicted. Overall, this thesis presents a dynamic model-building approach for multi-axis feed drive assemblies. The proposed model is general and can be configured according to the kinematic configuration. The model-building approach only requires the data from the servo system or auxiliary motion sensors, e.g., an IMU, which is non-intrusive and in favor of industrial implementation. Future research includes further investigation of the IMU measurement, geometric error identification, validation using more complicated feed drive system, and applications to the planning and monitoring of 5-axis machining process

A novel MRE adaptive seismic isolator using curvelet transform identification

Magnetorheological elastomeric (MRE) material is a novel type of material that can adap-tively change the rheological property rapidly, continuously, and reversibly when subjected to real-time external magnetic field. These new type of MRE materials can be developed by employing various schemes, for instance by mixing carbon nanotubes or acetone contents during the curing process which produces functionalized multiwall carbon nanotubes (MWCNTs). In order to study the mechanical and magnetic effects of this material, for potential application in seismic isolation, in this paper, different mathematical models of magnetorheological elastomers are analyzed and modified based on the reported studies on traditional magnetorheological elastomer. In this regard, a new feature identification method, via utilizing curvelet analysis, is proposed to make a multi-scale constituent analysis and subsequently a comparison between magnetorheological elastomer nanocomposite and traditional magnetorheological elastomers in a microscopic level. Furthermore, by using this “smart” material as the laminated core structure of an adaptive base isolation system, magnetic circuit analysis is numerically conducted for both complete and incomplete designs. Magnetic distribution of different laminated magnetorheological layers is discussed when the isolator is under compressive preloading and lateral shear loading. For a proof of concept study, a scaled building structure is established with the proposed isolation device. The dynamic performance of this isolated structure is analyzed by using a newly developed reaching law sliding mode control and Radial Basis Function (RBF) adaptive sliding mode control schemes. Transmissibility of the structural system is evaluated to assess its adaptability, controllability and nonlinearity. As the findings in this study show, it is promising that the structure can achieve its optimal and adaptive performance by designing an isolator with this adaptive material whose magnetic and mechanical properties are functionally enhanced as compared with traditional isolation devices. The adaptive control algorithm presented in this research can transiently suppress and protect the structure against non-stationary disturbances in the real time

Visual-Inertial first responder localisation in large-scale indoor training environments.

Accurately and reliably determining the position and heading of first responders undertaking training exercises can provide valuable insights into their situational awareness and give a larger context to the decisions made. Measuring first responder movement, however, requires an accurate and portable localisation system. Training exercises of- ten take place in large-scale indoor environments with limited power infrastructure to support localisation. Indoor positioning technologies that use radio or sound waves for localisation require an extensive network of transmitters or receivers to be installed within the environment to ensure reliable coverage. These technologies also need power sources to operate, making their use impractical for this application. Inertial sensors are infrastructure independent, low cost, and low power positioning devices which are attached to the person or object being tracked, but their localisation accuracy deteriorates over long-term tracking due to intrinsic biases and sensor noise. This thesis investigates how inertial sensor tracking can be improved by providing correction from a visual sensor that uses passive infrastructure (fiducial markers) to calculate accurate position and heading values. Even though using a visual sensor increase the accuracy of the localisation system, combining them with inertial sensors is not trivial, especially when mounted on different parts of the human body and going through different motion dynamics. Additionally, visual sensors have higher energy consumption, requiring more batteries to be carried by the first responder. This thesis presents a novel sensor fusion approach by loosely coupling visual and inertial sensors to create a positioning system that accurately localises walking humans in largescale indoor environments. Experimental evaluation of the devised localisation system indicates sub-metre accuracy for a 250m long indoor trajectory. The thesis also proposes two methods to improve the energy efficiency of the localisation system. The first is a distance-based error correction approach which uses distance estimation from the foot-mounted inertial sensor to reduce the number of corrections required from the visual sensor. Results indicate a 70% decrease in energy consumption while maintaining submetre localisation accuracy. The second method is a motion type adaptive error correction approach, which uses the human walking motion type (forward, backward, or sideways) as an input to further optimise the energy efficiency of the localisation system by modulating the operation of the visual sensor. Results of this approach indicate a 25% reduction in the number of corrections required to keep submetre localisation accuracy. Overall, this thesis advances the state of the art by providing a sensor fusion solution for long-term submetre accurate localisation and methods to reduce the energy consumption, making it more practical for use in first responder training exercises

Intelligent sensing for robot mapping and simultaneous human localization and activity recognition

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 2011.Thesis (Ph. D.) -- Bilkent University, 2011.Includes bibliographical references leaves 147-163.We consider three different problems in two different sensing domains, namely ultrasonic sensing and inertial sensing. Since the applications considered in each domain are inherently different, this thesis is composed of two main parts. The approach common to the two parts is that raw data acquired from simple sensors is processed intelligently to extract useful information about the environment. In the first part, we employ active snake contours and Kohonen’s selforganizing feature maps (SOMs) for representing and evaluating discrete point maps of indoor environments efficiently and compactly. We develop a generic error criterion for comparing two different sets of points based on the Euclidean distance measure. The point sets can be chosen as (i) two different sets of map points acquired with different mapping techniques or different sensing modalities, (ii) two sets of fitted curve points to maps extracted by different mapping techniques or sensing modalities, or (iii) a set of extracted map points and a set of fitted curve points. The error criterion makes it possible to compare the accuracy of maps obtained with different techniques among themselves, as well as with an absolute reference. We optimize the parameters of active snake contours and SOMs using uniform sampling of the parameter space and particle swarm optimization. A demonstrative example from ultrasonic mapping is given based on experimental data and compared with a very accurate laser map, considered an absolute reference. Both techniques can fill the erroneous gaps in discrete point maps. Snake curve fitting results in more accurate maps than SOMs because it is more robust to outliers. The two methods and the error criterion are sufficiently general that they can also be applied to discrete point maps acquired with other mapping techniques and other sensing modalities. In the second part, we use body-worn inertial/magnetic sensor units for recognition of daily and sports activities, as well as for human localization in GPSdenied environments. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. The error characteristics of the sensors are modeled using the Allan variance technique, and the parameters of lowand high-frequency error components are estimated. Then, we provide a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. Human activities are classified using five sensor units worn on the chest, the arms, and the legs. We compute a large number of features extracted from the sensor data, and reduce these features using both Principal Components Analysis (PCA) and sequential forward feature selection (SFFS). We consider eight different pattern recognition techniques and provide a comparison in terms of the correct classification rates, computational costs, and their training and storage requirements. Results with sensors mounted on various locations on the body are also provided. The results indicate that if the system is trained by the data of an individual person, it is possible to obtain over 99% correct classification rates with a simple quadratic classifier such as the Bayesian decision method. However, if the training data of that person are not available beforehand, one has to resort to more complex classifiers with an expected correct classification rate of about 85%. We also consider the human localization problem using body-worn inertial/ magnetic sensors. Inertial sensors are characterized by drift error caused by the integration of their rate output to get position information. Because of this drift, the position and orientation data obtained from inertial sensor signals are reliable over only short periods of time. Therefore, position updates from externally referenced sensors are essential. However, if the map of the environment is known, the activity context of the user provides information about position. In particular, the switches in the activity context correspond to discrete locations on the map. By performing activity recognition simultaneously with localization, one can detect the activity context switches and use the corresponding position information as position updates in the localization filter. The localization filter also involves a smoother, which combines the two estimates obtained by running the zero-velocity update (ZUPT) algorithm both forward and backward in time. We performed experiments with eight subjects in an indoor and an outdoor environment involving “walking,” “turning,” and “standing” activities. Using the error criterion in the first part of the thesis, we show that the position errors can be decreased by about 85% on the average. We also present the results of a 3-D experiment performed in a realistic indoor environment and demonstrate that it is possible to achieve over 90% error reduction in position by performing activity recognition simultaneously with localization.Altun, KeremPh.D

Driving Manoeuvre Recognition using Mobile Sensors

Automobiles are integral in today's society as they are used for transportation, commerce, and public services. The ubiquity of automotive transportation creates a demand for active safety technologies for the consumer. Recently, the widespread use and improved sensing and computing capabilities of mobile platforms have enabled the development of systems that can measure, detect, and analyze driver behaviour. Most systems performing driver behaviour analysis depend on recognizing driver manoeuvres. Improved accuracy in manoeuvre detection has the potential to improve driving safety, through applications such as monitoring for insurance, the detection of aggressive, distracted or fatigued driving, and for new driver training. This thesis develops algorithms for estimating vehicle kinematics and recognizing driver manoeuvres using a smartphone device. A kinematic model of the car is first introduced to express the vehicle's position and orientation. An Extended Kalman Filter (EKF) is developed to estimate the vehicle's positions, velocities, and accelerations using mobile measurements from inertial measurement units and the Global Positioning System (GPS). The approach is tested in simulation and validated on trip data using an On-board Diagnostic (OBD) device as the ground truth. The 2D state estimator is demonstrated to be an effective filter for measurement noise. Manoeuvre recognition is then formulated as a time-series classification problem. To account for an arbitrary orientation of the mobile device with respect to the vehicle, a novel method is proposed to estimate the phone's rotation matrix relative to the car using PCA on the gyroscope signal. Experimental results demonstrate that e Principal Component (PC) corresponds to a frame axis in the vehicle reference frame, so that the PCA projection matrix can be used to align the mobile device measurement data to the vehicle frame. A major impediment to classifier-manoeuvre recognition is the need for training data, specifically collecting enough data and generating an accurate ground truth. To address this problem, a novel training process is proposed to train the classifier using only simulation data. Training on simulation data bypasses these two issues as data can be cheaply generated and the ground truth is known. In this thesis, a driving simulator is developed using a Markov Decision Process (MDP) to generate simulated data for classifier training. Following training data generation, feature selection is performed using simple features such as velocity and angular velocity. A manoeuvre segmentation classifier is trained using multi-class SVMs. Validation was performed using data collected from driving sessions. A grid search was employed for parameter tuning. The classifier was found to have a 0.8158 average precision rate and a 0.8279 average recall rate across all manoeuvres resulting in an average F1 score of 0.8194 on the dataset