This thesis deals with design and productions of low-cost robot used for presentation and marketing purposes of companies CUTTER Systems spol. s r.o. and N-ROTE Mechanical s r.o. The main task of this robot is to serve beverages into glass containers.

Despite decades of research robotic manipulation systems outside of highly-structured industrial applications are still far from ubiquitous. Perhaps particularly curious is the fact that there appears to be a large divide between the theoretical grasp modeling literature and the practical manipulation community. Specifically, it appears that the most successful approaches to tasks such as pick-and-place or grasping in clutter are those that have opted for simple grippers or even suction systems instead of dexterous multi-fingered platforms. We argue that the reason for the success of these simple manipulation systemsis what we call passive stability: passive phenomena due to nonbackdrivable joints or underactuation allow for robust grasping without complex sensor feedback or controller design. While these effects are being leveraged to great effect, it appears the practical manipulation community lacks the tools to analyze them. In fact, we argue that the traditional grasp modeling theory assumes a complexity that most robotic hands do not possess and is therefore of limited applicability to the robotic hands commonly used today. We discuss these limitations of the existing grasp modeling literature and setout to develop our own tools for the analysis of passive effects in robotic grasping. We show that problems of this kind are difficult to solve due to the non-convexity of the Maximum Dissipation Principle (MDP), which is part of the Coulomb friction law. We show that for planar grasps the MDP can be decomposed into a number of piecewise convex problems, which can be solved for efficiently. Despite decades of research robotic manipulation systems outside of highlystructured industrial applications are still far from ubiquitous. Perhaps particularly curious is the fact that there appears to be a large divide between the theoretical grasp modeling literature and the practical manipulation community. Specifically, it appears that the most successful approaches to tasks such as pick-and-place or grasping in clutter are those that have opted for simple grippers or even suction systems instead of dexterous multi-fingered platforms. We argue that the reason for the success of these simple manipulation systemsis what we call passive stability: passive phenomena due to nonbackdrivable joints or underactuation allow for robust grasping without complex sensor feedback or controller design. While these effects are being leveraged to great effect, it appears the practical manipulation community lacks the tools to analyze them. In fact, we argue that the traditional grasp modeling theory assumes a complexity that most robotic hands do not possess and is therefore of limited applicability to the robotic hands commonly used today. We discuss these limitations of the existing grasp modeling literature and setout to develop our own tools for the analysis of passive effects in robotic grasping. We show that problems of this kind are difficult to solve due to the non-convexity of the Maximum Dissipation Principle (MDP), which is part of the Coulomb friction law. We show that for planar grasps the MDP can be decomposed into a number of piecewise convex problems, which can be solved for efficiently. We show that the number of these piecewise convex problems is quadratic in the number of contacts and develop a polynomial time algorithm for their enumeration. Thus, we present the first polynomial runtime algorithm for the determination of passive stability of planar grasps. For the spacial case we present the first grasp model that captures passive effects due to nonbackdrivable actuators and underactuation. Formulating the grasp model as a Mixed Integer Program we illustrate that a consequence of omitting the maximum dissipation principle from this formulation is the introduction of solutions that violate energy conservation laws and are thus unphysical. We propose a physically motivated iterative scheme to mitigate this effect and thus provide the first algorithm that allows for the determination of passive stability for spacial grasps with both fully actuated and underactuated robotic hands. We verify the accuracy of our predictions with experimental data and illustrate practical applications of our algorithm. We build upon this work and describe a convex relaxation of the Coulombfriction law and a successive hierarchical tightening approach that allows us to find solutions to the exact problem including the maximum dissipation principle. It is the first grasp stability method that allows for the efficient solution of the passive stability problem to arbitrary accuracy. The generality of our grasp model allows us to solve a wide variety of problems such as the computation of optimal actuator commands. This makes our framework a valuable tool for practical manipulation applications. Our work is relevant beyond robotic manipulation as it applies to the stability of any assembly of rigid bodies with frictional contacts, unilateral constraints and externally applied wrenches. Finally, we argue that with the advent of data-driven methods as well as theemergence of a new generation of highly sensorized hands there are opportunities for the application of the traditional grasp modeling theory to fields such as robotic in-hand manipulation through model-free reinforcement learning. We present a method that applies traditional grasp models to maintain quasi-static stability throughout a nominally model-free reinforcement learning task. We suggest that such methods can potentially reduce the sample complexity of reinforcement learning for in-hand manipulation.We show that the number of these piecewise convex problems is quadratic in the number of contacts and develop a polynomial time algorithm for their enumeration. Thus, we present the first polynomial runtime algorithm for the determination of passive stability of planar grasps.

This thesis is based upon one project, STEP (Sealing Technology – Enhanced Properties), inside ABB Corporate Research, Sweden, which is aiming to study the sealing technology of robotic implementation by a scientific/systematic approach. Nowadays the radial lip seal can be found in many fields such as automobiles, aero-planes, and marines, etc., and they have evolved into different designs and materials for the past several decades. Most of them are designed for conventional applications like cars, in which the shaft usually works in continuous rotary motions, and with such conditions lip seals could normally function well due to the sealing mechanism. However, regarding the robotic application, in which the shaft typically works in bi-directional and intermittent motions, the lip seal may not perform ideally and sometimes cause additional leakage. Hence, this thesis is mainly focused on an experimental approach to study the lip seal performance with controlled parameters, especially the robotic motion, together with other parameters, such as oil, temperature, pressure, etc. The work includes literature survey of lip seals, specifying the test rig, building the test rig, developing a leakage detection system, carrying out benchmark tests and seal tests with different robotic motions. / Detta examensarbete är baserat på ett så kallat STEP-projekt (Tätningsteknik – förstärkande egenskaper), inom ABB Corporate Research i Sverige, med syftet att studera tätningsteknik för robottillämpningar på ett vetenskapligt/systematiskt sätt. I dagsläget används radiella läpptätningar inom många områden, såsom fordons-, flyg- och fartygsindustri. Under de senaste decennierna har den vidareutvecklats och är nu gjord av flera olika material. De flesta är utformade för konventionella tillämpningar, som exempelvis fordon. Där arbetar axeln vanligen imedkontinuerligt roterande rörelser, och vid sådana förhållanden kan läpptätningar normalt fungera väl. Men när det gäller robottillämpningar, där axeln typiskt arbetar i dubbelriktade och intermittenta rörelser, fungerar läpptätningen inte idealt och kan även ge oönskat läckage. Detta examensarbete är fokuserat på att utveckla en experimentell metod för att studera läpptätningars prestanda med väl kontrollerade parametrar, såsom oljans egenskaper, temperatur, tryck, o.s.v. I arbetet ingår litteraturstudie av läpptätningar, specificering av testrigg, byggande av testrigg, utveckling av ett system för att detektera läckage, utförande av benchmark-tester, samt tätningsprov för olika robotrörelser.

Si les travaux menés dans le domaine des sciences de la décision ont permis de résoudre des problèmes variés, la capacité pour un agent à être autonome dans le choix de ses objectifs reste un problème difficile à traiter. Cette capacité est pourtant indispensable à l'autonomie dans son environnement pour un agent. Cette thèse a pour objectif de permettre à un robot de planifier ses actions pour de multiples objectifs contradictoires, dans un environement probabiliste et avec des ressources. La première contribution de ce travail est la proposition d'un nouveau modèle pour les objectifs, la motivation, constitué à partir d'un automate. Celui-ci décrit l'évolution de chaque objectif à l'aide d'états et de transitions correspondant à la réalisation de tâches, et définit les gains de récompenses. La deuxième contribution concerne une méthode de planification utilisant les motivations. Cette méthode est hiérarchique, proposant de résoudre la réalisation des tâches déterminantes pour les motivations dans un premier temps. En exploitant le contexte spécifique de chaque tâche, nous pouvons calculer rapidement leur politique et calculer leur modèle d'exécution, que nous appelons macro-action. Nous utilisons ensuite ces macro-actions dans un processus de planification local de haut-niveau, permettant de retourner un plan conditionnel des macro-actions à exécuter, que nous appelons agenda des macro-actions. Pour résoudre un problème dans sa longueur, nous proposons d'incluer ce système de planification dans une architecture délibérative qui permettra de contrôler l'exécution des agenda et d'en produire de nouveaux. / If the litterature of ​​decision-making has addressed a variety of problems, the ability to make an agent autonomous in the choice of its goals remains a difficult issue to address. This capacity is nevertheless essential for an agent in order to be automous in its environment. This thesis aims at allowing a robot to plan its own actions for multiple concurrent objectives, in a probabilistic environment and with resources. The first contribution of this work is the proposal of a new model for the objectives, the motivations, constituted from an automaton. Motivations describe the evolution of each objective using states and transitions corresponding to the realisation of tasks, and define the reward gains. The second contribution concerns a planning method that makes use the motivations. This method is hierarchical and proposes at first to solve the realisation of the tasks that are relevant for the motivations. By exploiting the specific context of each task, we can quickly compute their policy and calculate their execution model, which we call macro-action. We then use these macro-actions in a high-level local planning process, allowing us to return a conditional plan of the macro-actions to be executed, which we call macro-actions agenda. To solve a problem over time, we propose to include this planning system in a deliberative architecture that will control the execution of the agenda and produce new ones.

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Existing robotic arms have limited or no ability to avoid collisions with their environment due mainly to the lack of a suitable sensing system. A collision avoidance capability should be incorporated into every robot so that injuries to people and damage to equipment from collisions are prevented. Important applications that could benefit from robot collision avoidance include: manufacturing, robot-assisted surgery, robotic handling of hazardous waste, and personal robots. Creating a full-coverage, fast, reliable and cost effective sensing system for sensor-based robotic arm collision avoidance is a challenging problem. Capacitive sensors were selected based on their promising potential. Capacitive sensors have the limitations of nonlinearity and being influenced by the environment. In this thesis, their sensing behaviour, and solutions to these limitations, were investigated. A forward model predicts the capacitance for a given electrode geometry. The conventional method, Method of Moments (MoM) and Finite Element Method (FEM) were investigated and compared. The MoM demonstrated that the fringing electric field ignored by the conventional forward model is significant for the robotic arm application due to the relatively large ratio of electrode gap to electrode area. Two forward modeling cases were simulated by writing macro code for a commercial FEM package. The first consisted of two parallel cylindrical robotic arms. The second consisted of two cylindrical shell electrodes wrapped around a pair of robot links that rotated relative to each other. The results for this case were compared with experimental results. The FEM results were a poor predictor of the experimental results. The failure of the FEM model to include the true environmental conditions (e.g. air humidity and surrounding electric fields) is the most likely cause of its inaccuracy. An inverse capacitance model outputs the electrode geometry for a given capacitance. In this research the desired geometric output was the seven robot link pose variables, (x, y, z, q_x, q_y, q_z, q_0), describing the position and the orientation of the link of a robotic arm. A Cerebellar Model Articulation Controller (CMAC) neural network was chosen for the inverse modeling based its ability to model nonlinear behaviour and its efficiency. One CMAC network was trained for each pose variable. The sensor was built using capacitance sensing circuit and a multiplexor board with the potential for 16 by 16 electrode combinations. Note that an n by n combination produces n^2 separate capacitance values. For the inverse modeling experiments, four aluminum foil electrodes were mounted on a CRS-F3 robotic arm and four aluminum foil electrodes were placed on a wooden box used to simulate a second stationary robotic arm. A pair of reference electrodes was mounted on the back of the CRS-F3 arm. This reference measurement was used to normalize the measured capacitances in order to minimize environmental effects. The normalized capacitance data were used to train and test the CMAC neural networks. The CMAC learning factors were dynamically changed to reduce the training errors. A new fuzzy logic approach was developed that allowed the range of the CMAC input data to be increased without significantly increasing the training error. After evaluating eleven combinations of electrodes, it was determined that only the 3 by 3 and 4 by 4 combinations converged with small training errors. Three methods were used to analyze the CMAC testing errors: comparison plots, error plots and error metrics. Over a 15 cm range, pose variable y had maximum absolute errors of 2.1 mm for the 4 by 4 electrode combination and 7.2 mm for the 3 by 3 electrode combination. For the 4 by 4 combination the maximum relative errors were less than 3% for the x, y, and z variables, and less than 15% for the quaternion variables. For the 3 by 3 combination, these values increased to 13% and 20%, respectively. The larger relative errors for the quaternion variables were due to their smaller ranges of variation. Using the same hardware, a simple collision avoidance system was implemented using one pair of electrodes to detect the potential collision between a robotic arm moving in the vertical plane and a second stationary robot. The robot was shown to successfully avoid the potential collision and then continue its motion. / Thesis / Master of Applied Science (MASc)

As robotic systems grow in complexity, they inevitably undergo a process of specialization whereby they separate into an array of interconnected subsystems and individual processes. In order to function as a unified system, these processes rely heavily on interprocess communications (IPC) to transfer information between subsystems and various execution loops. This thesis presents the design, implementation, and validation of the Valor ROS Controller, a hybrid IPC interface layer and robot controller. The Valor ROS Controller connects the motion control system, implemented with the internally created Bifrost IPC, developed by Team VALOR for the DARPA Robotics Challenge (DRC) with the high level software developed by Team ViGIR that uses the Robot Operating System (ROS) IPC framework. The Valor ROS Controller also acts as a robot controller designed to run on THOR and ESCHER, and is configurable to use different control modes and controller implementations. By combining an IPC interface layer with controllers, the Valor ROS Controller enabled Team VALOR to use Team ViGIR's software capabilities at the DRC Finals. In addition to the qualitative validation of Team VALOR competing at the DRC Finals, this thesis studies the efficiency of the Valor ROS Controller by quantifying its computational resourceful utilization, message pathway latency, and joint controller tracking. Another contribution of this thesis is the quantification of end-effector pose error incurred by whole-body motions. This phenomenon has been observed on both THOR and ESCHER as one of their arms moves through a trajectory, however, it has never been studied in depth on either robot. The results demonstrate that the Valor ROS Controller adequately uses computational resources and has message latencies in the order of 50 ms. The results also indicate several avenues to improve arm tracking in Team VALOR's system. Whole-body motions account for approximately 5 cm of the end-effector pose error observed on hardware when an arm is at near full extension. / Master of Science

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