This thesis investigates the questions of where to grasp and how to grasp a given object with an articulated robotic grasping device. To this end, aspects of grasp synthesis and hand motion planning and control are investigated. Grasp synthesis is the process of determining a palm pose with respect to the target object, as well as a hand joint configuration and/or grasp contact points such that a successful grasp execution is allowed. Existing methods tackling the grasp synthesis problem can be categorized in analytical and empirical approaches. Analytical approaches are based on geometric, kinematic and/or dynamic formulations, whereas empirical methods aim at mimicking human strategies.An overarching idea throughout this thesis is to circumvent the curse of dimensionality, which is inherent in high-dimensional planning problems, by incorporating empirical data in analytical approaches. To this end, tools from the field of constrained optimization are used (i) to synthesize grasp families based on available prototype grasps, (ii) to incorporate heuristics capturing human grasp strategies in the grasp synthesis process and (iii) to encode demonstrated grasp motions in primitive motion controllers.The first contribution is related to the computation and analysis of grasp families which are represented as Independent Contact Regions (ICR) on a target object’s surface. To this end, the well-known concept of the Grasp Wrench Space for a single grasp is extended to be applicable for a set of grasps. Applications of ICR include grasp qualification by capturing the robustness of a grasp to position inaccuracies and the visual guidance of a demonstrator in a teleoperating scenario. In the second main contribution of this thesis, it is shown how to reduce the grasp solution space during the synthesis process by accounting for human approach strategies. This is achieved by imposing appropriate constraints to a corresponding optimization problem. A third contribution in this dissertation is made to reactive motion planning. Here, primitive controllers are synthesized by estimating the free parameters of corresponding dynamical systems from multiple demonstrated trajectories. The approach is evaluated on an anthropomorphic robot hand/arm platform. Also, an extension to a Model Predictive Control (MPC) scheme is presented which allows to incorporate state constraints for auxiliary tasks such as obstacle avoidance.

The rapid advancements in Robotics and Mixed Reality (MR) have opened new avenues for intuitive human-robot interaction. In this thesis, an intuitive and accessible robot grasping application is developed using MR to enable programming using the operator’s hand movements, reducing the technical complexity associated with traditional programming. The developed application leverages the strengths of MR to provide users with an immersive and intuitive environment. It includes powerful tools such as QR code recognition for quick deployment of virtual objects or the utilization of a Virtual Station that can be placed at any desired location, allowing remote and safe control of the robot. Three modes have been implemented, including manual target placing and thorough editing of its properties, path recording of the user's hand trajectory, and real-time replication of the operator's hand movements by the robot.  To assess the effectiveness and intuitiveness of the developed application, a series of user tests are presented. These evaluations include user feedback and task completion time compared to traditional programming methods, which provide valuable insights into the application's usability, efficiency, and user satisfaction. The intuitiveness of the developed application democratizes robot programming, expanding accessibility to a wider range of users, including inexperienced operators and students. / <p>Det finns övrigt digitalt material (t.ex. film-, bild- eller ljudfiler) eller modeller/artefakter tillhörande examensarbetet som ska skickas till arkivet.</p><p>There are other digital material (eg film, image or audio files) or models/artifacts that belongs to the thesis and need to be archived.</p><p>Utbytesstudenter</p>

<p dir="ltr">This dissertation presents the findings and research conducted during my Ph.D. study, which focuses on developing grasp policies for modular end-effectors on mobile manipulation platforms operating in cluttered environments. The primary objective of this research is to enhance the performance and accuracy of robotic manipulation systems in complex, real-world scenarios. The work has potential implications for various domains, including the rapidly growing Industry 4.0 and the advancement of autonomous systems in space habitats.</p><p dir="ltr">The dissertation offers a comprehensive literature review, emphasizing the challenges faced by mobile manipulation platforms in cluttered environments and the state-of-the-art techniques for grasping and manipulation. It showcases the development and evaluation of a Modular End-Effector System (MEES) for mobile manipulation platforms, which includes the investigation of object 6D pose estimation techniques, the generation of a deep learning-based grasping dataset for MEES, the development of a suction cup gripper grasping policy (Sim-Suction), the development of a two-finger grasping policy (Sim-Grasp), and the integration of Modular End-Effector System grasping policy (Sim-MEES). The proposed methodology integrates hardware designs, control algorithms, data-driven methods, and large language models to facilitate adaptive grasping strategies that consider the unique constraints and requirements of cluttered environments.</p><p dir="ltr">Furthermore, the dissertation discusses future research directions, such as further investigating the Modular End-Effector System grasping policy. This Ph.D. study aims to contribute to the advancement of robotic manipulation technology, ultimately enabling more versatile and robust mobile manipulation platforms capable of effectively interacting with complex environments.</p>

Today, most robots used in the industry are preprogrammed and require a welldefined and controlled environment. Reprogramming such robots is often a costly process requiring an expert. By enabling robots to learn tasks from human demonstration, robot installation and task reprogramming are simplified. In a longer time perspective, the vision is that robots will move out of factories into our homes and offices. Robots should be able to learn how to set a table or how to fill the dishwasher. Clearly, robot learning mechanisms are required to enable robots to adapt and operate in a dynamic environment, in contrast to the well defined factory assembly line. This thesis presents contributions in the field of robot task learning. A distinction is made between direct and indirect learning. Using direct learning, the robot learns tasks while being directly controlled by a human, for example in a teleoperative setting. Indirect learning, however, allows the robot to learn tasks by observing a human performing them. A challenging and realistic assumption that is decisive for the indirect learning approach is that the task relevant objects are not necessarily at the same location at execution time as when the learning took place. Thus, it is not sufficient to learn movement trajectories and absolute coordinates. Different methods are required for a robot that is to learn tasks in a dynamic home or office environment. This thesis presents contributions to several of these enabling technologies. Object detection and recognition are used together with pose estimation in a Programming by Demonstration scenario. The vision system is integrated with a localization module which enables the robot to learn mobile tasks. The robot is able to recognize human grasp types, map human grasps to its own hand and also evaluate suitable grasps before grasping an object. The robot can learn tasks from a single demonstration, but it also has the ability to adapt and refine its knowledge as more demonstrations are given. Here, the ability to generalize over multiple demonstrations is important and we investigate a method for automatically identifying the underlying constraints of the tasks. The majority of the methods have been implemented on a real, mobile robot, featuring a camera, an arm for manipulation and a parallel-jaw gripper. The experiments were conducted in an everyday environment with real, textured objects of various shape, size and color. / QC 20100706