Motion planning is an increasingly important field of research. Factory automation is becoming more prevalent and at the same time, production runs are shortening in the name of customisation. With computer controlled equipment becoming cheaper and more modular, setting up near-fully automated production lines is becoming fast and easy. This means that the actual programming of the robots and assembly system is becoming the rate determining step. Automated motion planning is a possible solution to this—but only if it can run fast enough. Many heuristic planners have been created in an attempt to achieve the necessary speeds in off-line (or more ambitiously, on-line) processing. This thesis aims to show that different types of heuristic planners can be designed to take advantage of specialised environments or robot characteristics. To show this, three distinct classes of heuristic planners are put forward for discussion. The first of these classes, addressed in Chapter 2, is of very generic planners which will work in virtually all situations (ie. almost any combination of robot and environment). This generality is obviously useful when lacking more specific domain knowledge. However these methods do suffer performance-wise in comparison with more specialised planners when there are characteristics of the problem which can be targeted. Chapter 3 moves to planners which are designed to specifically address certain peculiarities of the environment. Particular focus is given to environments whose corresponding configuration-spaces contain narrow gaps and passages. Finally Chapter 4 addresses a third class of planners: those which are designed for specific types of robots and movements. The particular focus is on locomotion for legged vehicles. For each of these three classes, some discussion is made of existing planners which can be so characterised. In addition, a novel algorithm is introduced in each as an example for particular consideration.

Soft robotic systems are becoming increasingly popular as they are generally safer, lighter, and easier to manufacture than their more rigid, traditional counterparts. These advantages allow an increased sense of freedom in both the design and operation of these platforms. In this work, we seek methods of leveraging this freedom to both design and plan motions for two different serial-chain, pneumatically actuated manipulators developed by Pneubotics, a small startup company based in San Francisco. In doing so, we focus primarily on two related endeavors: (1) the optimal kinematic design of these and other similar robots (i.e., choosing link lengths, base positioning, etc.), and (2) the planning of smooth paths in joint space that enable these robots to perform useful tasks. Our method of design optimization employs a genetic algorithm in combination with maximin multi-objective optimization techniques to efficiently generate a diverse set of Pareto optimal designs. This set represents the optimal region of the design space and highlights inherent tradeoffs that designers must make when choosing a particular set of design parameters for manufacture. In our work, we have chosen to optimize inflatable robots to be both dexterous, and to be able to support loads near the ground with limited deflection. We have also applied our framework to optimize a plastic manipulator to perform painting motions. In our approach to motion planning we simultaneously optimize the base position and joint motions of a robot in order to enable its end effector to follow a smooth desired trajectory. While this method of path planning generalizes to any kind of robot, we envision it to be especially applicable to soft robots and other mobile robots that can be quickly and easily repositioned to perform tasks in varying environments. Our method of path planning works by moving a set of virtual robot arms (each representing a single configuration in a sequence) branching from a common base, to a number of assigned target poses associated with a task. Additional goals and hard constraints (including joint limits) are naturally incorporated. The optimization problem at the core of this method is a quadratic program, allowing constrained high-dimensional problems to be solved in very little time. We demonstrate our method by planning and performing painting motion on two different systems. We also demonstrate in simulation how our planner could be used to perform several common tasks including those involving, pick-and-place, wiping and wrapping motions.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第24604号 / 工博第5110号 / 新制||工||1978(附属図書館) / 京都大学大学院工学研究科機械理工学専攻 / (主査)教授 松野 文俊, 教授 小森 雅晴, 教授 神田 崇行 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

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Many autonomous robots operating in unknown and unstructured environments rely on building a dense 3D map of it during exploration. What tasks the robot can perform depends on the information stored in this map. Most 3D maps currently in use store information required for robot control and environment reconstruction – is this point in space occupied, or safe to navigate to? To enable more complex tasks additional information is required. We introduce Semantic UFOMap, an open-source octree based mapping framework designed for online use on limited hardware. Capable of real-time fusion and querying of semantic instances into the map – enabling high-level robot tasks and human-robot interaction. The online capabilities are evaluated using ground-truth data, where we show competitive results compared to voxel hashing, with optimizations still available. Additionally, we demonstrate a potential application with a simulated autonomous exploration and object navigation experiment. The evaluation shows that Semantic UFOMap is capable of real-time online performance. Storing semantic information in the map has the potential to open up new autonomous robot applications and yield improvements in existing tasks. / Autonoma robotar som opererar i okända och ostrukturerade mijöer är ofta beroende av att skapa en 3D-karta under utforskning av området. Villka uppgifter roboten kan utföra beror på informationen som finns tillgänglig i kartan. De flesta nuvarande kartor som används sparar information som behövs för säker navigation och miljörekonstruktion – är den här positionen ett hinder, eller är den säker att navigera till? För att möjligjöra mer komplexa uppgifter behöver roboten ha tillgång till ytterligare information. Vi presenterar Semantic UFOMap, ett öppen källkods kartläggnings ramverk för realtids användning på begränsad hårdvara. Genom att klara av realtids integrering och sökning av semantiska instanser i kartan möjliggör ramverket mer komplexa uppgifter och öppnar upp fler användningsområden i människa-robot interaktion. Utvärdering görs med hjälp av inspelad data, vi visar konkurrenskraftiga resultat jämfört med voxel hashning, med optimering fortfarande tillgänglig. Ett användningsområde demonstreras med ett simulerat autonomt utforsknings och objektnavigerings experiment. Utvärderingen visar att Semantic UFOMap klarar av realtids applikationer. Att spara semantisk information i kartan har potential att öppna upp för nya användningsområden inom robotik och leda till förbättringar i befintliga uppgifter.

Cette thèse aborde les problèmes inverses de contrôle optimal (IOCP) pour trouver les fonctions de coûts pour lesquelles les mouvements humains sont optimaux. En supposant que les observations de mouvements humains sont parfaites, alors que le processus de commande du moteur humain est imparfait, nous proposons un algorithme de commande approximative optimale. En appliquant notre algorithme pour les observations de mouvement humaines collectées: mouvement du bras humain au cours d'une tâche de vissage industrielle, une tâche de suivi visuel d’une cible et une tâche d'initialisation de la marche, nous avons effectué une analyse en boucle ouverte. Pour les trois cas, notre algorithme a trouvé les fonctions de coût qui correspondent mieux ces données, tout en satisfaisant approximativement les Karush-Kuhn-Tucker (KKT) conditions d'optimalité. Notre algorithme offre un beau temps de calcul pour tous les cas, fournir une opportunité pour son utilisation dans les applications en ligne. Pour la tâche de suivi visuel d’une cible, nous avons étudié une modélisation en boucle fermée avec deux boucles de rétroaction PD. Avec des données artificielles, nous avons obtenu des résultats cohérents en termes de tendances des gains et les critères trouvent par notre algorithme pour la tâche de suivi visuel d’une cible. Dans la seconde partie de notre travail, nous avons proposé une nouvelle approche pour résoudre l’IOCP, dans un cadre d'erreur bornée. Dans cette approche, nous supposons que le processus de contrôle moteur humain est parfait tandis que les observations ont des erreurs et des incertitudes d'agir sur eux, étant imparfaite. Les erreurs sont délimitées avec des limites connues, sinon inconnu. Notre approche trouve l'ensemble convexe de de fonction de coût réalisables avec la certitude qu'il comprend la vraie solution. Nous numériquement garanties en utilisant des outils d'analyse d'intervalle. / This thesis addresses inverse optimal control problems (IOCP) to find the cost functions for which the human motions are optimal. Assuming that the human motion observations are perfect, while the human motor control process is imperfect, we propose an approximately optimal control algorithm. By applying our algorithm to the human motion observations collected for: the human arm trajectories during an industrial screwing task, a postural coordination in a visual tracking task and a walking gait initialization task, we performed an open loop analysis. For the three cases, our algorithm returned the cost functions which better fit these data, while approximately satisfying the Karush-Kuhn-Tucker (KKT) optimality conditions. Our algorithm offers a nice computational time for all cases, providing an opportunity for its use in online applications. For the visual tracking task, we investigated a closed loop modeling with two PD feedback loops. With artificial data, we obtained consistent results in terms of feedback gains’ trends and criteria exhibited by our algorithm for the visual tracking task. In the second part of our work, we proposed a new approach to solving the IOCP, in a bounded error framework. In this approach, we assume that the human motor control process is perfect while the observations have errors and uncertainties acting on them, being imperfect. The errors are bounded with known bounds, otherwise unknown. Our approach finds the convex hull of the set of feasible cost function with a certainty that it includes the true solution. We numerically guaranteed this using interval analysis tools.