Modelling and Interactional Control of a Multi-fingered Robotic Hand for Grasping and Manipulation.

PhDIn this thesis, the synthesis of a grasping and manipulation controller of the Barrett hand, which is an archetypal example of a multi-fingered robotic hand, is investigated in some detail. This synthesis involves not only the dynamic modelling of the robotic hand but also the control of the joint and workspace dynamics as well as the interaction of the hand with object it is grasping and the environment it is operating in. Grasping and manipulation of an object by a robotic hand is always challenging due to the uncertainties, associated with non-linearities of the robot dynamics, unknown location and stiffness parameters of the objects which are not structured in any sense and unknown contact mechanics during the interaction of the hand’s fingers and the object. To address these challenges, the fundamental task is to establish the mathematical model of the robot hand, model the body dynamics of the object and establish the contact mechanics between the hand and the object. A Lagrangian based mathematical model of the Barrett hand is developed for controller implementation. A physical SimMechanics based model of the Barrett hand is also developed in MATLAB/Simulink environment. A computed torque controller and an adaptive sliding model controller are designed for the hand and their performance is assessed both in the joint space and in the workspace. Stability analysis of the controllers are carried out before developing the control laws. The higher order sliding model controllers are developed for the position control assuming that the uncertainties are in place. Also, this controllers enhance the performance by reducing chattering of the control torques applied to the robot hand. A contact model is developed for the Barrett hand as its fingers grasp the object in the operating environment. The contact forces during the simulation of the interaction of the fingers with the object were monitored, for objects with different stiffness values. Position and force based impedance controllers are developed to optimise the contact force. To deal with the unknown stiffness of the environment, adaptation is implemented by identifying the impedance. An evolutionary algorithm is also used to estimate the desired impedance parameters of the dynamics of the coupled robot and compliant object. A Newton-Euler based model is developed for the rigid object body. A grasp map and a hand Jacobian are defined for the Barrett hand grasping an object. A fixed contact model with friction is considered for the grasping and the manipulation control. The compliant dynamics of Barrett hand and object is developed and the control problem is defined in terms of the contact force. An adaptive control framework is developed and implemented for different grasps and manipulation trajectories of the Barrett hand. The adaptive controller is developed in two stages: first, the unknown robot and object dynamics are estimated and second, the contact force is computed from the estimated dynamics. The stability of the controllers is ensured by applying Lyapunov’s direct method

Grasp Point Optimization by Online Exploration of Unknown Object Surface

Li Q, Haschke R, Bolder B, Ritter H. Grasp Point Optimization by Online Exploration of Unknown Object Surface. Presented at the IEEE-RAS International Conference on Humanoid Robots, Osaka

Robot dexterity: from deformable grasping to impulsive manipulation

Nowadays, it is fairly common for robots to manipulate different objects and perform sophisticated tasks. They lift up massive hard and soft objects, plan the motion with specific speed, and repeat complex tasks with high precision. However, without carefully control, even the most sophisticated robots would not be able to achieve a simple task. Robot grasping of deformable objects is an under-researched area. The difficulty comes from both mechanics and computation. First, deformation caused by grasping motions changes the global geometry of the object. Second, different from rigid body grasping whose torques are invariant, the torques exerted by the grasping fingers vary during the deformation. Collision is a common phenomenon in robot manipulation that takes place when objects collide together, as observed in the games of marbles, billiards, and bowling. To make the robot purposefully make use of impact to perform better at certain tasks, a general and computationally efficient model is needed for predicting the outcome of impact. And also, tasks to alter the trajectory of a flying object are also common in our daily life, like batting a baseball, playing ping-pong ball. A good motion planning strategy based on impact is necessary for the robots to accomplish these tasks. The thesis investigates problems of deformable grasping and impact-based manipulation on rigid bodies. The work contains deformable grasping on 2D and 3D soft objects, multi-body collision modeling, and motion planning of batting a flying object. In the first part of the thesis, in 2D space an algorithm is proposed to characterize the best resistance by a grasp to an adversary finger which minimizes the work done by the grasping fingers. An optimization scheme is offered to handle the general case of frictional segment contact. And also, an efficient squeeze-and-test strategy is introduced for a two-finger robot hand to grasp and lift a 3D deformable object resting on the plane. Next, an $n$-body impulse-based collision model that works with or without friction is studied. The model could be used to determine the post-collision motions of any number of objects engaged in the collision. Making use of the impact model, the final part of the thesis investigated the task of batting a flying object with a manipulator. First, motion planning of the task in 2D space is studied. In the frictionless case, a closed-form solution is analyzed, simulated, and validated via the task of a WAM Arm batting a hexagonal object. In the frictional case, contact friction introduces a continuum of solutions, from which we select the one that expends the minimum kinetic energy of the manipulator. Next, analyses and results are generalized to 3D. Without friction the problem ends up with one-dimensional set of solution, from which optimum is obtained. For frictional case hitting normal is fixed for simplicity. The system is then transferred to a root-finding problem, and Newton\u27s method is applied to find the optimal planning

Nonprehensile Manipulation via Multisensory Learning from Demonstration

Dexterous manipulation problem concerns control of a robot hand to manipulate an object in a desired manner. While classical dexterous manipulation strategies are based on stable grasping (or force closure), many human-like manipulation tasks do not maintain grasp stability, and often utilize the intrinsic dynamics of the object rather than the closed form of kinematic relation between the object and the robotic fingers. Such manipulation strategies are referred as nonprehensile or dynamic dexterous manipulation in the literature. Nonprehensile manipulation typically involves fast and agile movements such as throwing and flipping. Due to the complexity of such motions (which may involve impulsive dynamics) and uncertainties associated with them, it has been challenging to realize nonprehensile manipulation tasks in a reliable way. In this paper, we propose a new control strategy to realize practical nonprehensile manipulation tasks using a robot hand. The main idea of our control strategy are two-folds. Firstly, we make explicit use of multiple modalities of sensory data for the design of control law. Specifically, force data is employed for feedforward control while the position data is used for feedback (i.e. reactive) control. Secondly, control signals (both feedback and feedforward) are obtained by the multisensory learning from demonstration (LfD) experiments which are designed and performed for specific nonprehensile manipulation tasks in concern. We utilize various LfD frameworks such as Gaussian mixture model and Gaussian mixture regression (GMM/GMR) and hidden Markov model and GMR (HMM/GMR) to reproduce generalized motion profiles from the human expert's demonstrations. The proposed control strategy has been verified by experimental results on dynamic spinning task using a sensory-rich two-finger robotic hand. The control performance (i.e. the speed and accuracy of the spinning task) has also been compared with that of the classical dexterous manipulation based on finger gating

Machine Systems for Exploration and Manipulation: A Conceptual Framework and Method of Evaluation

A conceptual approach to describing and evaluating problem-solving by robotic systems is offered. One particular problem of importance to the field of robotics, disassembly, is considered. A general description is provided of an effector system equipped with sensors that interacts with objects for purposes of disassembly and that learns as a result. The system\u27s approach is bottom up, in that it has no a priori knowledge about object categories. It does, however, have pre-existing methods and strategies for exploration and manipulation. The sensors assumed to be present are vision, proximity, tactile, position, force, and thermal. The system\u27s capabilities are described with respect to two phases: object exploration and manipulation. Exploration takes the form of executing exploratory procedures, algorithms for determining the substance, structure, and mechanical properties of objects. Manipulation involves manipulatory operators, defined by the type of motion, nature of the end-effector configuration, and precise parameterization. The relation of the hypothesized system to existing implementations is described, and a means of evaluating it is also proposed

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development