Integrasjon av et minimalistisk sett av sensorer for kartlegging og lokalisering av landbruksroboter

Robots have recently become ubiquitous in many aspects of daily life. For in-house applications there is vacuuming, mopping and lawn-mowing robots. Swarms of robots have been used in Amazon warehouses for several years. Autonomous driving cars, despite being set back by several safety issues, are undeniably becoming the standard of the automobile industry. Not just being useful for commercial applications, robots can perform various tasks, such as inspecting hazardous sites, taking part in search-and-rescue missions. Regardless of end-user applications, autonomy plays a crucial role in modern robots. The essential capabilities required for autonomous operations are mapping, localization and navigation. The goal of this thesis is to develop a new approach to solve the problems of mapping, localization, and navigation for autonomous robots in agriculture. This type of environment poses some unique challenges such as repetitive patterns, large-scale sparse features environments, in comparison to other scenarios such as urban/cities, where the abundance of good features such as pavements, buildings, road lanes, traffic signs, etc., exists. In outdoor agricultural environments, a robot can rely on a Global Navigation Satellite System (GNSS) to determine its whereabouts. It is often limited to the robot's activities to accessible GNSS signal areas. It would fail for indoor environments. In this case, different types of exteroceptive sensors such as (RGB, Depth, Thermal) cameras, laser scanner, Light Detection and Ranging (LiDAR) and proprioceptive sensors such as Inertial Measurement Unit (IMU), wheel-encoders can be fused to better estimate the robot's states. Generic approaches of combining several different sensors often yield superior estimation results but they are not always optimal in terms of cost-effectiveness, high modularity, reusability, and interchangeability. For agricultural robots, it is equally important for being robust for long term operations as well as being cost-effective for mass production. We tackle this challenge by exploring and selectively using a handful of sensors such as RGB-D cameras, LiDAR and IMU for representative agricultural environments. The sensor fusion algorithms provide high precision and robustness for mapping and localization while at the same time assuring cost-effectiveness by employing only the necessary sensors for a task at hand. In this thesis, we extend the LiDAR mapping and localization methods for normal urban/city scenarios to cope with the agricultural environments where the presence of slopes, vegetation, trees render the traditional approaches to fail. Our mapping method substantially reduces the memory footprint for map storing, which is important for large-scale farms. We show how to handle the localization problem in dynamic growing strawberry polytunnels by using only a stereo visual-inertial (VI) and depth sensor to extract and track only invariant features. This eliminates the need for remapping to deal with dynamic scenes. Also, for a demonstration of the minimalistic requirement for autonomous agricultural robots, we show the ability to autonomously traverse between rows in a difficult environment of zigzag-liked polytunnel using only a laser scanner. Furthermore, we present an autonomous navigation capability by using only a camera without explicitly performing mapping or localization. Finally, our mapping and localization methods are generic and platform-agnostic, which can be applied to different types of agricultural robots. All contributions presented in this thesis have been tested and validated on real robots in real agricultural environments. All approaches have been published or submitted in peer-reviewed conference papers and journal articles.Roboter har nylig blitt standard i mange deler av hverdagen. I hjemmet har vi støvsuger-, vaske- og gressklippende roboter. Svermer med roboter har blitt brukt av Amazons varehus i mange år. Autonome selvkjørende biler, til tross for å ha vært satt tilbake av sikkerhetshensyn, er udiskutabelt på vei til å bli standarden innen bilbransjen. Roboter har mer nytte enn rent kommersielt bruk. Roboter kan utføre forskjellige oppgaver, som å inspisere farlige områder og delta i leteoppdrag. Uansett hva sluttbrukeren velger å gjøre, spiller autonomi en viktig rolle i moderne roboter. De essensielle egenskapene for autonome operasjoner i landbruket er kartlegging, lokalisering og navigering. Denne type miljø gir spesielle utfordringer som repetitive mønstre og storskala miljø med få landskapsdetaljer, sammenlignet med andre steder, som urbane-/bymiljø, hvor det finnes mange landskapsdetaljer som fortau, bygninger, trafikkfelt, trafikkskilt, etc. I utendørs jordbruksmiljø kan en robot bruke Global Navigation Satellite System (GNSS) til å navigere sine omgivelser. Dette begrenser robotens aktiviteter til områder med tilgjengelig GNSS signaler. Dette vil ikke fungere i miljøer innendørs. I ett slikt tilfelle vil reseptorer mot det eksterne miljø som (RGB-, dybde-, temperatur-) kameraer, laserskannere, «Light detection and Ranging» (LiDAR) og propriopsjonære detektorer som treghetssensorer (IMU) og hjulenkodere kunne brukes sammen for å bedre kunne estimere robotens tilstand. Generisk kombinering av forskjellige sensorer fører til overlegne estimeringsresultater, men er ofte suboptimale med hensyn på kostnadseffektivitet, moduleringingsgrad og utbyttbarhet. For landbruksroboter så er det like viktig med robusthet for lang tids bruk som kostnadseffektivitet for masseproduksjon. Vi taklet denne utfordringen med å utforske og selektivt velge en håndfull sensorer som RGB-D kameraer, LiDAR og IMU for representative landbruksmiljø. Algoritmen som kombinerer sensorsignalene gir en høy presisjonsgrad og robusthet for kartlegging og lokalisering, og gir samtidig kostnadseffektivitet med å bare bruke de nødvendige sensorene for oppgaven som skal utføres. I denne avhandlingen utvider vi en LiDAR kartlegging og lokaliseringsmetode normalt brukt i urbane/bymiljø til å takle landbruksmiljø, hvor hellinger, vegetasjon og trær gjør at tradisjonelle metoder mislykkes. Vår metode reduserer signifikant lagringsbehovet for kartlagring, noe som er viktig for storskala gårder. Vi viser hvordan lokaliseringsproblemet i dynamisk voksende jordbær-polytuneller kan løses ved å bruke en stereo visuel inertiel (VI) og en dybdesensor for å ekstrahere statiske objekter. Dette eliminerer behovet å kartlegge på nytt for å klare dynamiske scener. I tillegg demonstrerer vi de minimalistiske kravene for autonome jordbruksroboter. Vi viser robotens evne til å bevege seg autonomt mellom rader i ett vanskelig miljø med polytuneller i sikksakk-mønstre ved bruk av kun en laserskanner. Videre presenterer vi en autonom navigeringsevne ved bruk av kun ett kamera uten å eksplisitt kartlegge eller lokalisere. Til slutt viser vi at kartleggings- og lokaliseringsmetodene er generiske og platform-agnostiske, noe som kan brukes med flere typer jordbruksroboter. Alle bidrag presentert i denne avhandlingen har blitt testet og validert med ekte roboter i ekte landbruksmiljø. Alle forsøk har blitt publisert eller sendt til fagfellevurderte konferansepapirer og journalartikler

Localising Faster: Efficient and precise lidar-based robot localisation in large-scale environments

This paper proposes a novel approach for global localisation of mobile robots in large-scale environments. Our method leverages learning-based localisation and filtering-based localisation, to localise the robot efficiently and precisely through seeding Monte Carlo Localisation (MCL) with a deep learned distribution. In particular, a fast localisation system rapidly estimates the 6-DOF pose through a deep-probabilistic model (Gaussian Process Regression with a deep kernel), then a precise recursive estimator refines the estimated robot pose according to the geometric alignment. More importantly, the Gaussian method (i.e. deep probabilistic localisation) and non-Gaussian method (i.e. MCL) can be integrated naturally via importance sampling. Consequently, the two systems can be integrated seamlessly and mutually benefit from each other. To verify the proposed framework, we provide a case study in large-scale localisation with a 3D lidar sensor. Our experiments on the Michigan NCLT long-term dataset show that the proposed method is able to localise the robot in 1.94 s on average (median of 0.8 s) with precision 0.75 m in a large-scale environment of approximately 0.5 km 2

Localization for mobile robots using panoramic vision, local features and particle filter

In this paper we present a vision-based approach to self-localization that uses a novel scheme to integrate feature-based matching of panoramic images with Monte Carlo localization. A specially modified version of Lowe’s SIFT algorithm is used to match features extracted from local interest points in the image, rather than using global features calculated from the whole image. Experiments conducted in a large, populated indoor environment (up to 5 persons visible) over a period of several months demonstrate the robustness of the approach, including kidnapping and occlusion of up to 90% of the robot’s field of view

Supplementation With Hydrogen Sulfide Helps Mitigate The Effects Of Ischemia Reperfusion Injury In A Model Of Donation After Cardiac Death Renal Transplantation

Donation after cardiac death (DCD) grafts experience prolonged ischemia reperfusion injury (IRI) leading to higher rates of delayed graft function and failure. Recent studies have reported protective effects of hydrogen sulfide (H2S) against IRI. Our study aims at improving DCD renal graft outcomes by H2S supplementation in an in-vivo murine model of renal transplantation (RTx) and study the underlying mechanism in an in-vitro model using porcine kidney proximal-tubular-epithelial cells (LLC-PK1). H2S provided survival benefit, improved renal graft function and decreased renal injury in recipient rats. In our in-vitro model of LLC-PK1 cells, H2S demonstrated an important role mediated by mitochondria in the pathophysiological effects of IRI by reducing depolarization of the mitochondrial membrane and the amount of reactive oxygen species. In the long run, these findings would help bridge the gap between organ demand and supply by reducing the extent of renal IRI and delayed graft function in DCD donors

Detection of tightly closed flaws by nondestructive testing (NDT) methods in steel and titanium

X-radiographic, liquid penetrant, ultrasonic, eddy current and magnetic particle testing techniques were optimized and applied to the evaluation of 4340 steel (180 KSI-UTS) and 6Al-4V titanium (STA) alloy specimens. Sixty steel specimens containing a total of 176 fatigue cracks and 60 titanium specimens containing a total of 135 fatigue cracks were evaluated. The cracks ranged in length from .043 cm (0.017 inch) to 1.02 cm (.400 inch) and in depth from .005 cm (.002 inch) to .239 cm (.094 inch) for steel specimens. Lengths ranged from .048 cm (0.019 inch) to 1.03 cm (.407 inch) and depths from 0.010 cm (.004 inch) to .261 cm (0.103 inch) for titanium specimens. Specimen thicknesses were nominally .152 cm (0.060 inch) and 0.635 cm (0.250 inch) and surface finishes were nominally 125 rms. Specimens were evaluated in the "as machined" surface condition, after etch surface and after proof loading in a randomized inspection sequence

Prolyl-isomerase Pin1 controls normal and cancer stem cells of the breast by counteracting the Fbxw7-oncosuppressive barrier on the Notch signalling pathway

2012/2013Cancer stem cells (CSCs) are proposed to be responsible for breast cancer heterogeneity, chemotherapeutic treatment failure, metastatic spread and disease recurrence. The precise identification of the molecular bases that govern the induction and maintenance of CSCs and their aggressive phenotypes is of utmost importance, since it may provide the rational to develop effective therapeutic strategies. In particular there is a considerable effort in finding common pathways, mutations or histological features that might be targeted for therapy, overcoming breast cancer heterogeneity. Here we now demonstrate that CSC self-renewal, chemoresistance, tumour growth and metastases formation capabilities’ are under direct control of Pin1’s enzymatic activity on the Notch signalling pathway. In particular Pin1 protects the nuclear activated forms of Notch1 and Notch4 (N1/4-ICD) from their E3-ubiquitin-ligase Fbxw7α, thereby boosting their protein levels and transcriptional activity. Fbxw7α acts as a potent inhibitor of CSCs maintenance by promoting protein degradation of N1- and N4-ICD, and, as a consequence, this ubiquitin-ligase strongly decreased tumour growth and metastases dissemination in vivo. Interestingly, concomitant over-expression of Pin1 almost completely recovered all these aggressive breast cancer traits. In tissues from breast cancer patients, we observed Notch signalling over-activation despite presence of the negative regulator Fbxw7α, which relied on high Pin1 protein levels. Notably, activation of the Notch-Pin1 axis correlated with poor prognosis in these patients. As a consequence of our findings, suppression of Pin1 holds promise in reverting aggressive phenotypes in breast cancer though shrinkage of CSCs number and a concomitant gain in chemosensitivity, carrying important implications for breast cancers therapy.XXVI Ciclo198

Methods to identify novel α-helical peptides that bind to LMO4

Protein-protein interactions (PPIs) play an essential role in regulating cells. LIM Domain-Only protein 4 (LMO4) is a transcriptional co-regulatory protein that functions entirely through PPIs. It contributes to developmental processes and the pathogenesis of breast cancer, although the mechanisms are only partially known. The development of inhibitors of LMO4 could form useful research tools for understanding these mechanisms and may form the basis of new breast cancer therapies. Previously, the Matthews Laboratory has developed peptide inhibitors of LMO4 based on natural binding partners (β-strand peptides). The hypotheses is that α-helical peptides can be more specific than β-strand peptides, it should be possible to generate α-helical peptide binders of LMO4 using natural α-helices as templates. The aims were to develop two methods to identify novel α-helical peptides that bind to LMO4: a split EGFP complementation (spEGFP) system providing a high through-put method for the initial screening of a library of α-helical peptides; and, a Yeast Two Hybrid Competition Assay (Y2HCA) as an orthogonal method to validate hits and provide an assessment of their relative binding affinities. For the Y2HCA, a new construct was added to create a series of constructs that relatively assess affinities of weakly binding peptides. The spEGFP system was expanded, introducing controls and a construct to screen a library of α-helical peptides for affinity to LMO4LIM1. Naturally occurring α-helices were considered and tested as potential templates. Chemical transformation and DNA extraction protocols for BL-21(DE3) cells were developed to enable efficient library expression and screening. This thesis provides mechanisms to identify α-helical peptides that bind to LMO4 and the ground work for a high-throughput library screen

Robust Localization in 3D Prior Maps for Autonomous Driving.

In order to navigate autonomously, many self-driving vehicles require precise localization within an a priori known map that is annotated with exact lane locations, traffic signs, and additional metadata that govern the rules of the road. This approach transforms the extremely difficult and unpredictable task of online perception into a more structured localization problem—where exact localization in these maps provides the autonomous agent a wealth of knowledge for safe navigation. This thesis presents several novel localization algorithms that leverage a high-fidelity three-dimensional (3D) prior map that together provide a robust and reliable framework for vehicle localization. First, we present a generic probabilistic method for localizing an autonomous vehicle equipped with a 3D light detection and ranging (LIDAR) scanner. This proposed algorithm models the world as a mixture of several Gaussians, characterizing the z-height and reflectivity distribution of the environment—which we rasterize to facilitate fast and exact multiresolution inference. Second, we propose a visual localization strategy that replaces the expensive 3D LIDAR scanners with significantly cheaper, commodity cameras. In doing so, we exploit a graphics processing unit to generate synthetic views of our belief environment, resulting in a localization solution that achieves a similar order of magnitude error rate with a sensor that is several orders of magnitude cheaper. Finally, we propose a visual obstacle detection algorithm that leverages knowledge of our high-fidelity prior maps in its obstacle prediction model. This not only provides obstacle awareness at high rates for vehicle navigation, but also improves our visual localization quality as we are cognizant of static and non-static regions of the environment. All of these proposed algorithms are demonstrated to be real-time solutions for our self-driving car.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133410/1/rwolcott_1.pd

COMPARISON OF NEUTRON NON DESTRUCTIVE METHOD AND CONVENTIONAL CHEMICAL METHOD FOR CHLORIDE MEASUREMENT IN CONCRETE

The presence of chloride in concrete is a critical issue raising concerns in the construction industry as they promote corrosion of the steel reinforcements, drastically reducing the strength of the structure. The aim of this study is to compare the performance of a neutron-based nondestructive testing method, Prompt Gamma Activation Analysis (PGAA) against the destructive wet chemistry method ASTM C-1152 currently used to determine the chloride concentration in concrete. Two modes of PGAA operation were tested. One was to use PGAA with a slit collimator to measure the chlorides at 2 mm thick cross-section in intact samples. The other was a direct comparison with C-1152 to analyze powdered concrete samples. Concrete was prepared in four batches, in which three batches had added chloride -at nominally 0.2%, 0.1%, 0.01% by weight of cement and the fourth (control) batch has zero added. The PGAA analysis was done at the Cold Neutron PGAA station at NIST and the C1152 testing was done at the National Ready Mixed Concrete Association (NRMCA) laboratory. The intact samples were scanned at three different vertical positions. The PGAA method is capable of detecting Cl at levels corresponding to the corrosion threshold of 0.1-0.2% Cl by weight of cement. The minimum detectable limit for PGAA is below 0.02% Cl by weight of cement and approaches the Cl background contributed by the raw materials, in this case, the cement. The PGAA- measured chlorides concentrations showed excellent linearity after correction for the chloride content in the concrete raw materials, mainly the cement. For the powdered samples, the C1152 and PGAA results were in very good agreement. However, the PGAA data showed much less scatter with an uncertainty as low as 0.3%. The findings of this study indicate that PGAA is a feasible replacement for the C1152 method and since it can be done on intact specimens, it avoids the time-consuming steps of crushing, sieving and nitric acid extraction and can be more cost-effective