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No description available.

This report presents the construction of a two-wheeled self balancing robot with the ability to handle an uneven load. Two-wheeled self-balancing robots have proven to be a potential solution to the problem of efficient warehouse management, thanks to their ability to navigate tight spaces quickly and efficiently while balancing their load. The research questions defined include the ability to develop an algorithm to dynamically adjust the robot’s tilt angle, how an uneven load affects the robot’s stability, and whether a PID controller is sufficient in this context. The project was limited to constructing a prototype that could balance with an external load, with a budget of 1,000 Swedish kronor and a four-month timeframe. By testing and evaluating different control algorithms, the robot’s performance was presented in terms of stability and efficiency. The implementation of PID control was successful, and the robot was able to balance as a result. However, the goal of handling an uneven load was not met without the implementation of an additional algorithm to dynamically adjust the robot’s tilt angle. With these two control techniques, the robot was able to balance with and without an added load with good stability. To evaluate performance, tests were performed with the load placed centered and off-centered on the robot’s top plate. The results of the tests showed that the robot was able to dynamically adjust its tilt angle to balance with added weight without affecting stability. / Denna rapport presenterar konstruktionen av en tvåhjulig självbalanserande robot med förmågan att hantera snedfördelad last. Tvåhjuliga självbalanserande robotar har visat sig vara en potentiell lösning på problemet kring effektiv lagerhantering tack vare deras förmåga att hantera snäva utrymmen på ett snabbt och energieffektivt sätt samtidigt som lasten balanseras. Forskningsfrågorna som definierades inkluderar möjligheten att framställa en algoritm för att dynamiskt ställa in robotens lutningsvinkel, hur ojämn last påverkar robotens stabilitet, och om en PID-kontroller är tillräcklig i detta sammanhang. Projektet begränsades till att konstruera en prototyp som klarar av att balansera med extern last, med en budget på 1000 svenska kronor och en tidsram på fyra månader. Genom att testa och utvärdera olika kontrollalgoritmer presenterades robotens prestanda i termer av stabilitet och effektivitet. Roboten balanserade tack vare implementeringen av PID-reglering, men önskemålet om snedfördelad last uppfylldes inte och det krävdes ytterligare en algoritm för att dynamiskt reglera robotens lutningsvinkel. Med hjälp av dessa två reglertekniker kunde roboten balansera både med och utan adderad last med god stabilitet. Tester utfördes för att utvärdera prestandan när lasten var placerad både centrerat och ocentrerat på robotens topplatta. Resultaten visade att roboten kan dynamiskt anpassa lutningsvinkeln för att balansera med tillagd vikt utan att stabiliteten påverkas.

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AH Automation har fått i uppdrag från Dahréntråd AB att genomföra en förstudie och ta fram ett automationskoncept för automatisk hantering av spolar runt en spoltvätt. Dahréntråd har för avsikt att upphandla en ny spoltävtt. Detta examensarbete är genomfört med avsikten att genomföra denna förstudie för AH Automation med målsättningen att det leder till ett effektivt system för automatisk hantering runt tvätten. Resultatet i denna rapport är ett koncept och kräver ytterligare arbete innan projektet kan realiseras. Många timmar arbete har spenderats på rundvandringar, diskussioner, möten, analys och ”trial & error” iteration framtagning. Detta har lett fram till en helautomatiserad lösning. Resultatet innefattar en robot som lastar in spolar i tvätten och en robot på en linjärenhet som lastar spolar från tvätten. Roboten innan spoltvätten sorterar pallar och mellanlägg samtidigt som den förser roboten efter tvätten med pallar och mellanlägg. Båda robotarna är utrustade med ett vakuumgripdon som har för avsikt att kunna hantera samtliga spole modeller, pallar och mellanlägg. Konceptet tillåter att spolar kan plockas manuellt när packningen inte är kompatibel med robotplock. Resultatet presenteras som en 3D layout skapad i Inventor Autodesk. / AH Automation has been commissioned by Dahréntråd AB to carry out a pilot study and develop an automation concept for automatic handling of bobbins around an industrial bobbin washer. Dahréntråd intends to buy a new bobbin washing machine. This thesis report is made with the intention of carrying out this pilot study for AH Automation with the aim that it leads to a effective automation system. The result in this report is a concept and requires further work before the project can be realized. Many hours of work have been spent on tours, discussions, meeting, analysis and trial & error iteration development. These steps have led to a fully automated solution. The result includes a robot that loads bobbins into the washing machine and one robot on a linear unit, that unloads the bobbins from the washing machine. The first robot is sorting pallets and slipsheets at the same time as it supplies the other robot with pallets and slipsheets. Both robots are equipped with a vacuum gripper that aims to handle all bobbin models, pallets and slipsheets. This concept also allows bobbins to be picked manually with lifting assistance when the bobbins are packed in such a way that that the robot can’t handle it.

The objective of the work described in this thesis is the development of a configuration estimation scheme for quasi soft-bodied robots, with the end goal being accurate soft-robot proprioception to enable robotic manipulation of unknown external loads. The first chapter introduces the problem and state-of-the-art modeling methods. The second chapter presents work we did building on previous research on a novel sensing scheme for soft-bodied robotic configuration estimation. The third chapter discusses the development of a geometric shape-sensing model based on overlapping tendon-length measurements. This model is geometrically exact for a body composed of two constant curvature segments in bending. The next chapter discusses our implemention of this model on a real world system and tested in two and three dimensions. We estimated the shape of a 215 mm long robotic segment with less then 3 mm of median error for a set of 50 configurations causing motion in two dimensions and approximately 27 mm of median translational error in three dimensions. The final chapter draws conclusions and proposes future work to allow for full robotic proprioception.

The study presents the use of a humanoid robot to facilitate social engagement behaviors in four children with autism. These children were enrolled in a semester long treatment program based on components of the SCERTS model designed to facilitate social communication (Prizant, 2003). Following baseline, children received intervention sessions with and without the robot. During sessions involving the robot, each child would participate in a 10 minute interaction (as part of a 50 minute sessions) using a robot to facilitate interaction with a graduate clinician or parent. The interactions were recorded and analyzed for occurrences of social engagement behaviors. This study focused specifically on the triadic interaction that occurred in the pre-and post-intervention sessions. The triadic interaction was a structured play sequence involving three individuals (the child, the graduate clinician, and the assisting graduate clinician). The results suggest that the robot has potential to facilitate reciprocal action between children with autism and adults.

Depuis plusieurs années la robotique est vue comme une solution clef pour améliorer la qualité de vie des personnes ayant subi une amputation. Pour créer de nouvelles prothèses intelligentes qui peuvent être facilement intégrées à la vie quotidienne et acceptée par ces personnes, celles-ci doivent être non-intrusives, fiables et peu coûteuses. L’électromyographie de surface fournit une interface intuitive et non intrusive basée sur l’activité musculaire de l’utilisateur permettant d’interagir avec des robots. Cependant, malgré des recherches approfondies dans le domaine de la classification des signaux sEMG, les classificateurs actuels manquent toujours de fiabilité, car ils ne sont pas robustes face au bruit à court terme (par exemple, petit déplacement des électrodes, fatigue musculaire) ou à long terme (par exemple, changement de la masse musculaire et des tissus adipeux) et requiert donc de recalibrer le classifieur de façon périodique. L’objectif de mon projet de recherche est de proposer une interface myoélectrique humain-robot basé sur des algorithmes d’apprentissage par transfert et d’adaptation de domaine afin d’augmenter la fiabilité du système à long-terme, tout en minimisant l’intrusivité (au niveau du temps de préparation) de ce genre de système. L’aspect non intrusif est obtenu en utilisant un bracelet à électrode sèche possédant dix canaux. Ce bracelet (3DC Armband) est de notre (Docteur Gabriel Gagnon-Turcotte, mes co-directeurs et moi-même) conception et a été réalisé durant mon doctorat. À l’heure d’écrire ces lignes, le 3DC Armband est le bracelet sans fil pour l’enregistrement de signaux sEMG le plus performant disponible. Contrairement aux dispositifs utilisant des électrodes à base de gel qui nécessitent un rasage de l’avant-bras, un nettoyage de la zone de placement et l’application d’un gel conducteur avant l’utilisation, le brassard du 3DC peut simplement être placé sur l’avant-bras sans aucune préparation. Cependant, cette facilité d’utilisation entraîne une diminution de la qualité de l’information du signal. Cette diminution provient du fait que les électrodes sèches obtiennent un signal plus bruité que celle à base de gel. En outre, des méthodes invasives peuvent réduire les déplacements d’électrodes lors de l’utilisation, contrairement au brassard. Pour remédier à cette dégradation de l’information, le projet de recherche s’appuiera sur l’apprentissage profond, et plus précisément sur les réseaux convolutionels. Le projet de recherche a été divisé en trois phases. La première porte sur la conception d’un classifieur permettant la reconnaissance de gestes de la main en temps réel. La deuxième porte sur l’implémentation d’un algorithme d’apprentissage par transfert afin de pouvoir profiter des données provenant d’autres personnes, permettant ainsi d’améliorer la classification des mouvements de la main pour un nouvel individu tout en diminuant le temps de préparation nécessaire pour utiliser le système. La troisième phase consiste en l’élaboration et l’implémentation des algorithmes d’adaptation de domaine et d’apprentissage faiblement supervisé afin de créer un classifieur qui soit robuste au changement à long terme. / For several years, robotics has been seen as a key solution to improve the quality of life of people living with upper-limb disabilities. To create new, smart prostheses that can easily be integrated into everyday life, they must be non-intrusive, reliable and inexpensive. Surface electromyography provides an intuitive interface based on a user’s muscle activity to interact with robots. However, despite extensive research in the field of sEMG signal classification, current classifiers still lack reliability due to their lack of robustness to short-term (e.g. small electrode displacement, muscle fatigue) or long-term (e.g. change in muscle mass and adipose tissue) noise. In practice, this mean that to be useful, classifier needs to be periodically re-calibrated, a time consuming process. The goal of my research project is to proposes a human-robot myoelectric interface based on transfer learning and domain adaptation algorithms to increase the reliability of the system in the long term, while at the same time reducing the intrusiveness (in terms of hardware and preparation time) of this kind of systems. The non-intrusive aspect is achieved from a dry-electrode armband featuring ten channels. This armband, named the 3DC Armband is from our (Dr. Gabriel Gagnon-Turcotte, my co-directors and myself) conception and was realized during my doctorate. At the time of writing, the 3DC Armband offers the best performance for currently available dry-electrodes, surface electromyographic armbands. Unlike gel-based electrodes which require intrusive skin preparation (i.e. shaving, cleaning the skin and applying conductive gel), the 3DC Armband can simply be placed on the forearm without any preparation. However, this ease of use results in a decrease in the quality of information. This decrease is due to the fact that the signal recorded by dry electrodes is inherently noisier than gel-based ones. In addition, other systems use invasive methods (intramuscular electromyography) to capture a cleaner signal and reduce the source of noises (e.g. electrode shift). To remedy this degradation of information resulting from the non-intrusiveness of the armband, this research project will rely on deep learning, and more specifically on convolutional networks. The research project was divided into three phases. The first is the design of a classifier allowing the recognition of hand gestures in real-time. The second is the implementation of a transfer learning algorithm to take advantage of the data recorded across multiple users, thereby improving the system’s accuracy, while decreasing the time required to use the system. The third phase is the development and implementation of a domain adaptation and self-supervised learning to enhance the classifier’s robustness to long-term changes.

There are several industries exploring solutions to quickly and accurately digitize unexplored indoor environments, into useable three-dimensional databases. Unfortunately, there are inherent challenges to the indoor mapping process such as, scanning limitations and environment complexity, which require a specific application of tools to map an environment precisely with low cost and high speed. This thesis successfully demonstrates the design and implementation of a low cost mobile robotic computing platform with laser scanner, for quickly mapping with high resolution, urban and/or indoor environments using a gyro-enhanced orientation sensor and selectable levels of detail. In addition, a low cost alternative solution to three-dimensional laser scanning is presented, via a standard two-dimensional SICK proximity laser scanner mounted to a custom servo motor mount and controlled by external microcontroller. A software system to control the robot is presented, which incorporates and adheres to widely accepted software engineering guidelines and principles. An analysis of the overall system, including robot specifications, system capabilities, and justification for certain design decisions, are described in detail. Results of various open source software algorithms, as it applies to scan data and image data, are also compared; including evaluation of data correlation and registration techniques. In addition, laser scanner mapping tests, specifications, and capabilities are presented and analyzed. A sample design for converting the final scanned point cloud data to a database is presented and assessed. The results suggest the overall project yields a relatively high degree of accuracy and lower cost over most other existing systems surveyed, as well as, the potential for application of the system in other fields. The results also discuss thoughts for possible future research work.