Active Searching of RFID Chips by a Group of Relatively Stabilized Helicopters

V této práci je představeno nové řešení problému lokalizace RF vysílačů. Cílovou aplikací je hledání objektu, který byl označen vysílačem, v dané oblasti, kde je GPS nedostupná nebo když je vyžadována vyšší přesnost lokalizace. Využití může nalézt například pro vyhledávání vojáků na bojišti, aut v zastavěných oblastech, nástrojů na staveništích, zvířat apod. Současné metody vyhledávání RF vysílaču jsou obvykle založeny na vytvoření mapy hodnot RSSI v této oblasti předem, a následném porovnávání RSSI měření při lokalizaci s touto mapou pro nalezení polohy s nejlépe odpovídající hodnotou. Další metody jsou založeny například na trilateraci polohy ze vzdáleností mezi vysílači a přijímači, které mohou být měřeny různými způsoby. Všechny tyto metody ale většinou využívají stacionární přijímače rozmístěné v oblasti a obecně vyžadují časově náročné přípravy. Řešení, prezentované v této práci, využívá k vyhledávání vysílačů v oblasti formaci bezpilotních helikoptér, nesoucích RF přijímače. Je založeno na měření RSSI v různých místech v oblasti pomocí těchto helikoptér a na Kalmanově Filtru. Simulace a experimenty, které jsou popsané v této práci, ukazují, že navrhovaný algoritmus je použitelný pro cílovou aplikaci a že co do přesnosti a robustnosti může konkurovat ostatním současným lokalizačním algoritmům bez toho, aby vyžadoval předpřipravenou infrastrukturu.A novel solution to the problem of localizing RF transmitters is presented in this thesis. Target application is the ability to find an object, which has been tagged with a transmitter, in a desired area, where the GPS may be unavailable or when better precision is required. It may find use for example in finding soldiers in the field, cars in urban areas, tools in construction sites, animals, etc. Contemporary methods for localizing RF transmitters are usually based on creating a map of RSSI values in an area beforehand and then comparing it to the measurements when localizing to find the best matching position, or less frequently on trilateration from some form of distance measurements. They mostly rely on stationary receivers placed in the area and generally require time consuming setup and preparation. The presented solution utilizes a formation of MAVs (Micro Aerial Vehicles), carrying RF receivers, to scout the area for transmitters and report their positions. It is based on the Kalman Filter and relies on measuring RSSI with the MAVs at different positions in the area. The precision and robustness of the algorithm is on the same level with state-of-the-art localization algorithms, however, the new proposed algorithm does not require any preinstalled infrastructure, which makes it much easier and cheaper to implement in a variety of locations

Flexible Stereo: Constrained, Non-rigid, Wide-baseline Stereo Vision for Fixed-wing Aerial Platforms

This paper proposes a computationally efficient method to estimate the time-varying relative pose between two visual-inertial sensor rigs mounted on the flexible wings of a fixed-wing unmanned aerial vehicle (UAV). The estimated relative poses are used to generate highly accurate depth maps in real-time and can be employed for obstacle avoidance in low-altitude flights or landing maneuvers. The approach is structured as follows: Initially, a wing model is identified by fitting a probability density function to measured deviations from the nominal relative baseline transformation. At run-time, the prior knowledge about the wing model is fused in an Extended Kalman filter~(EKF) together with relative pose measurements obtained from solving a relative perspective N-point problem (PNP), and the linear accelerations and angular velocities measured by the two inertial measurement units (IMU) which are rigidly attached to the cameras. Results obtained from extensive synthetic experiments demonstrate that our proposed framework is able to estimate highly accurate baseline transformations and depth maps.Comment: Accepted for publication in IEEE International Conference on Robotics and Automation (ICRA), 2018, Brisban

GP-SLAM+: real-time 3D lidar SLAM based on improved regionalized Gaussian process map reconstruction

This paper presents a 3D lidar SLAM system based on improved regionalized Gaussian process (GP) map reconstruction to provide both low-drift state estimation and mapping in real-time for robotics applications. We utilize spatial GP regression to model the environment. This tool enables us to recover surfaces including those in sparsely scanned areas and obtain uniform samples with uncertainty. Those properties facilitate robust data association and map updating in our scan-to-map registration scheme, especially when working with sparse range data. Compared with previous GP-SLAM, this work overcomes the prohibitive computational complexity of GP and redesigns the registration strategy to meet the accuracy requirements in 3D scenarios. For large-scale tasks, a two-thread framework is employed to suppress the drift further. Aerial and ground-based experiments demonstrate that our method allows robust odometry and precise mapping in real-time. It also outperforms the state-of-the-art lidar SLAM systems in our tests with light-weight sensors.Comment: Accepted by IROS 202

Efficient 3D Segmentation, Registration and Mapping for Mobile Robots

Sometimes simple is better! For certain situations and tasks, simple but robust methods can achieve the same or better results in the same or less time than related sophisticated approaches. In the context of robots operating in real-world environments, key challenges are perceiving objects of interest and obstacles as well as building maps of the environment and localizing therein. The goal of this thesis is to carefully analyze such problem formulations, to deduce valid assumptions and simplifications, and to develop simple solutions that are both robust and fast. All approaches make use of sensors capturing 3D information, such as consumer RGBD cameras. Comparative evaluations show the performance of the developed approaches. For identifying objects and regions of interest in manipulation tasks, a real-time object segmentation pipeline is proposed. It exploits several common assumptions of manipulation tasks such as objects being on horizontal support surfaces (and well separated). It achieves real-time performance by using particularly efficient approximations in the individual processing steps, subsampling the input data where possible, and processing only relevant subsets of the data. The resulting pipeline segments 3D input data with up to 30Hz. In order to obtain complete segmentations of the 3D input data, a second pipeline is proposed that approximates the sampled surface, smooths the underlying data, and segments the smoothed surface into coherent regions belonging to the same geometric primitive. It uses different primitive models and can reliably segment input data into planes, cylinders and spheres. A thorough comparative evaluation shows state-of-the-art performance while computing such segmentations in near real-time. The second part of the thesis addresses the registration of 3D input data, i.e., consistently aligning input captured from different view poses. Several methods are presented for different types of input data. For the particular application of mapping with micro aerial vehicles where the 3D input data is particularly sparse, a pipeline is proposed that uses the same approximate surface reconstruction to exploit the measurement topology and a surface-to-surface registration algorithm that robustly aligns the data. Optimization of the resulting graph of determined view poses then yields globally consistent 3D maps. For sequences of RGBD data this pipeline is extended to include additional subsampling steps and an initial alignment of the data in local windows in the pose graph. In both cases, comparative evaluations show a robust and fast alignment of the input data

A quantitative taxonomy of human hand grasps

Background: A proper modeling of human grasping and of hand movements is fundamental for robotics, prosthetics, physiology and rehabilitation. The taxonomies of hand grasps that have been proposed in scientific literature so far are based on qualitative analyses of the movements and thus they are usually not quantitatively justified. Methods: This paper presents to the best of our knowledge the first quantitative taxonomy of hand grasps based on biomedical data measurements. The taxonomy is based on electromyography and kinematic data recorded from 40 healthy subjects performing 20 unique hand grasps. For each subject, a set of hierarchical trees are computed for several signal features. Afterwards, the trees are combined, first into modality-specific (i.e. muscular and kinematic) taxonomies of hand grasps and then into a general quantitative taxonomy of hand movements. The modality-specific taxonomies provide similar results despite describing different parameters of hand movements, one being muscular and the other kinematic. Results: The general taxonomy merges the kinematic and muscular description into a comprehensive hierarchical structure. The obtained results clarify what has been proposed in the literature so far and they partially confirm the qualitative parameters used to create previous taxonomies of hand grasps. According to the results, hand movements can be divided into five movement categories defined based on the overall grasp shape, finger positioning and muscular activation. Part of the results appears qualitatively in accordance with previous results describing kinematic hand grasping synergies. Conclusions: The taxonomy of hand grasps proposed in this paper clarifies with quantitative measurements what has been proposed in the field on a qualitative basis, thus having a potential impact on several scientific fields

There and Back Again: Exploring the Roles of Models and Natural History in Macroevolution

Ecological diversity in nature is tremendously complex. Evolutionary biologists and ecologists have sought to understand this complexity using foundational concepts like ecological niches, guilds, and adaptive zones. The merger of these concepts with stochastic models and phylogenies helped create the field of phylogenetic comparative methods, which has made fundamental contributions to our understanding of the evolutionary history of life’s rich ecological variety and the role ecology plays in the diversification of species and phenotypes and the assembly of species-rich communities. Despite this progress, however, phylogenetic comparative methods have been slow to expand their data repertoire. There is a general rarity of comparative datasets that include primary natural history observations of organisms in nature and of comparative methods to work with such data. The main contribution of this dissertation is to address this shortfall. I do so in three main ways. First, in earlier chapters I study some simple stochastic models of ecological character state change, revealing unappreciated subtleties that complicate our ability to interpret their results in terms of historical events. Second, building off lessons learned from these early chapters, I develop a new method that uses primary natural history observations to jointly infer the phylogenetic distribution of ecological niche states for individual species and their unsampled ancestors. Third, to demonstrate the flexibility of the new method, I conduct an empirical analysis on the diversification of snake feeding habits using a new comprehensive database of observations of prey acquisition by snakes that I compiled. Taken together, the research in this dissertation demonstrates how fundamental observations of organisms in nature can be used to make quantitative inferences about the macroevolution of complex ecological traits and suggests new ways of integrating natural history data into comparative biology.PHDEcology and Evolutionary BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163161/1/mgru_1.pd

A Study of Myoelectric Signal Processing

This dissertation of various aspects of electromyogram (EMG: muscle electrical activity) signal processing is comprised of two projects in which I was the lead investigator and two team projects in which I participated. The first investigator-led project was a study of reconstructing continuous EMG discharge rates from neural impulses. Related methods for calculating neural firing rates in other contexts were adapted and applied to the intramuscular motor unit action potential train firing rate. Statistical results based on simulation and clinical data suggest that performances of spline-based methods are superior to conventional filter-based methods in the absence of decomposition error, but they unacceptably degrade in the presence of even the smallest decomposition errors present in real EMG data, which is typically around 3-5%. Optimal parameters for each method are found, and with normal decomposition error rates, ranks of these methods with their optimal parameters are given. Overall, Hanning filtering and Berger methods exhibit consistent and significant advantages over other methods. In the second investigator-led project, the technique of signal whitening was applied prior to motion classification of upper limb surface EMG signals previously collected from the forearm muscles of intact and amputee subjects. The motions classified consisted of 11 hand and wrist actions pertaining to prosthesis control. Theoretical models and experimental data showed that whitening increased EMG signal bandwidth by 65-75% and the coefficients of variation of temporal features computed from the EMG were reduced. As a result, a consistent classification accuracy improvement of 3-5% was observed for all subjects at small analysis durations (\u3c 100 ms). In the first team-based project, advanced modeling methods of the constant posture EMG-torque relationship about the elbow were studied: whitened and multi-channel EMG signals, training set duration, regularized model parameter estimation and nonlinear models. Combined, these methods reduced error to less than a quarter of standard techniques. In the second team-based project, a study related biceps-triceps surface EMG to elbow torque at seven joint angles during constant-posture contractions. Models accounting for co-contraction estimated that individual flexion muscle torques were much higher than models that did not account for co-contraction

Optimal state observation using quadratic boundedness: application to UAV disturbance estimation

This paper presents the design of a state observer which guarantees quadratic boundedness of the estimation error. By using quadratic Lyapunov stability analysis, the convergence rate and the ultimate (steady-state) error bounding ellipsoid are identified as the parameters that define the behaviour of the estimation. Then, it is shown that these objectives can be merged in a scalarised objective function with one design parameter, making the design problem convex. In the second part of the article, a UAV model is presented which can be made linear by considering a particular state and frame of reference. The UAV model is extended to incorporate a disturbance model of variable size. The joint model matches the structure required to derive an observer, following the lines of the proposed design approach. An observer for disturbances acting on the UAV is derived and the analysis of the performances with respect to the design parameters is presented. The effectiveness and main characteristics of the proposed approach are shown using simulation results.Peer ReviewedPostprint (author's final draft