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This book is about automatic handling of non-rigid or deformable objects like cables, fabric, or foam rubber. The automation by robots in industrial environments, is especially examined. It discusses several important automation aspects, such as material modelling and simulation, planning and control strategies, collaborative systems, and industrial applications. This book collects contributions from various countries and international projects and, therefore, provides a representative overview of the state of the art in this field. It is of particular interest for scientists and practitioners in the area of robotics and automation
Methods by which robots can learn control laws that enable real-time reactivity using dynamical systems; with applications and exercises. This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans. It introduces a set of control laws that enable reactivity using dynamical systems, a widely used method for solving motion-planning problems in robotics. These control approaches can replan in milliseconds to adapt to new environmental constraints and offer safe and compliant control of forces in contact. The techniques offer theoretical advantages, including convergence to a goal, non-penetration of obstacles, and passivity. The coverage of learning begins with low-level control parameters and progresses to higher-level competencies composed of combinations of skills. Learning for Adaptive and Reactive Robot Control is designed for graduate-level courses in robotics, with chapters that proceed from fundamentals to more advanced content. Techniques covered include learning from demonstration, optimization, and reinforcement learning, and using dynamical systems in learning control laws, trajectory planning, and methods for compliant and force control . Features for teaching in each chapter: applications, which range from arm manipulators to whole-body control of humanoid robots; pencil-and-paper and programming exercises; lecture videos, slides, and MATLAB code examples available on the author’s website . an eTextbook platform website offering protected material[EPS2] for instructors including solutions.
Robots are often used in industry to handle flexible objects, such as frames, beams, thin plates, rubber tubes, leather goods and composite materials. Moving long flexible objects in a desired path and also precise positioning and orienting the objects need a collaborative action between two robot arms. Most of the earlier studies have dealt with manipulation of rigid objects and only a few have focused on the collaborative manipulators handling flexible objects. Such studies on handling of flexible objects generally used finite element method or assumed mode method for deriving the dynamic model of the flexible objects. These approximation methods require more number of sensors to feedback the vibration measurements or require an observer. Unlike in the earlier studies, this thesis concerns with development of a dynamic model of the flexible object in partial differential equation (PDE) form and design of a robust control strategy for collaborative manipulation of the flexible objects by two rigid robot arms. Two planar rigid manipulators each with three links and revolute joints handling a flexible object is considered during the model development. Kinematic and dynamic equations of the flexible object are derived without using any approximation techniques. The resulting dynamic equation of the flexible object together with the manipulator dynamic equations form the combined dynamic model of the system. The developed complete system of dynamic equations is described by the PDE's having rigid as well as flexible parameters coupled together. Such a coupled system must be controlled without using any form of approximation techniques and this is accomplished using the singular perturbation approach. By utilizing this technique, slow and fast subsystems are identified in two different time scales and controller is designed for each subsystem. The key issue in developing a control algorithm is that, it should be robust against uncertain parameters of the manipulators and the flexible object and it should also achieve the exponential convergence. Hence, for the slow subsystem, sliding mode control algorithm is developed and for the fast subsystem, a simple feedback control algorithm is designed. In general, usage of singular perturbation technique necessitates exponential stability of the slow and fast subsystems, which is evaluated by satisfying the Tikhnov's theorem. Hence, the exponential stability analysis for both the subsystems is performed. Simulation results are presented to validate the composite control scheme. As a further consideration in the improvement of control law for the slow subsystem, two modified control algorithms are suggested. The first one focused on the avoidance of velocity signal measurement which is useful to eliminate the need of velocity sensors and the second controller aims at avoiding the complex regressor in the control law. The capability of those controllers is illustrated through simulation studies. The extension of earlier analysis has been carried out by developing the complete system of dynamic equations in joint space.
Robot interaction control is one of the most challenging targets for industrial robotics. While it would provide the robotic systems with a high degree of autonomy, its effectiveness is limited by the complexity of this problem and by the necessity of special sensors (six-dof force sensors). On the other hand, the control methodologies to be adopted for addressing this problem can be considered mature and well-assessed. All the known interaction control strategies (e.g. impedance, direct force control) are tackled and reshuffled in a geometrically consistent way for simplification of the task specification and enhancement of the execution performance. This book represents the first step towards the application of theoretical results at an industrial level; in fact each proposed control algorithm is experimentally tested here on an industrial robotic setup.
Recent advances in RbD have identified a number of key issues for ensuring a generic approach to the transfer of skills across various agents and contexts. This book focuses on the two generic questions of what to imitate and how to imitate and proposes active teaching methods.
Remote operation of nuclear processes started with electromechanical telemanipulation over 60 years ago. Today, robotic manipulation is essential to several steps of the nuclear fuel cycle, decontamination and decommissioning operations, and many other related industries. However, not all tasks can be accomplished with a fully remote system. This research focuses on the control of a robotic manipulator in direct contact with human operators to improve the safety and throughput of a precision assembly task. Humans are naturally talented at precise force modulation, using tactile feedback and intuition to assemble complex and fragile parts. Robots, on the other hand, outrank humans in positional precision and strength, especially over long periods. By using a robotic system to offload the weight and reduce the inertia of an object, a human can focus on interaction forces and complex maneuvers to better complete precision assembly and insertion tasks. To investigate the validity of this claim, a custom admittance controller was applied to a passively-balanced collaborative robotic manipulator. Experimental results were collected in a blind precision insertion task with a heavy payload and fragile insertion member. In this pilot study, operator performance was assessed with the manipulator in both active and passive states, a passive mechanical gantry with counterweight for gravity compensation, and no assisting mechanisms. Experimental results indicate improvement in success rates, operation times, and physical effort
Robots don't always need expensive, dedicated fixtures for workpart positioning; table-top manipulation is possible and the sliding that occurs can be used to advantage if it is well understood. The author offers methods of automating the design of robot manipulation strategies reliant on sliding and friction. Annotation copyrighted by Book News, Inc., Portland, OR
While humans and other social animals (such as ants) can easily form teams to move heavy or bulky objects, robots struggle in collaborative manipulation tasks, especially under partial information about the environment or the object being transported. This thesis looks to enable flexible, scalable coordination in robot teams, looking toward a future where robots not only move objects together, but also work together to perform autonomous assembly of structures and manufactured goods. In Part I of this thesis, we investigate methods for collaborative manipulation and grasp synthesis under considerable uncertainty about the object's size and physical properties. Using tools from nonlinear control, we present a novel decentralized adaptive controller for collaborative manipulation that allows a team of robots to asymptotically track a desired trajectory in SE$(3)$. We also study the problem of synthesizing robust grasps of objects using only RGB images; we present a method which leverages a novel learned object representation to generate risk-sensitive grasps which can reason about the ambiguity inherent in the object shape. In Part II of this thesis, we turn our attention to the problem of multi-robot assembly planning. We show this problem can be posed as a mixed-integer linear program, which can be solved to global optimality using commercial solvers, and present effective heuristic strategies which can be computed quickly. Further, we present a method that uses supervised learning to accelerate the online solution of general mixed-integer convex programs using offline data. We show our method provides significant speedups over commercial solvers in a variety of robotics problems, including grasp selection and task allocation.
Tutors can design entry-level courses in robotics with a strong orientation to the fundamental discipline of manipulator control pdf solutions manual Overheads will save a great deal of time with class preparation and will give students a low-effort basis for more detailed class notes Courses for senior undergraduates can be designed around Parts I – III; these can be augmented for masters courses using Part IV