Tactile-Proprioceptive Robotic Grasping

Robotic grasping has been studied increasingly for a few decades. While progress has been made in this field, robotic hands are still nowhere near the capability of human hands. However, in the past few years, the increase in computational power and the availability of commercial tactile sensors have made it easier to develop techniques that exploit the feedback from the hand itself, the sense of touch. The focus of this thesis lies in the use of this sense. The work described in this thesis focuses on robotic grasping from two different viewpoints: robotic systems and data-driven grasping. The robotic systems viewpoint describes a complete architecture for the act of grasping and, to a lesser extent, more general manipulation. Two central claims that the architecture was designed for are hardware independence and the use of sensors during grasping. These properties enables the use of multiple different robotic platforms within the architecture. Secondly, new data-driven methods are proposed that can be incorporated into the grasping process. The first of these methods is a novel way of learning grasp stability from the tactile and haptic feedback of the hand instead of analytically solving the stability from a set of known contacts between the hand and the object. By learning from the data directly, there is no need to know the properties of the hand, such as kinematics, enabling the method to be utilized with complex hands. The second novel method, probabilistic grasping, combines the fields of tactile exploration and grasp planning. By employing well-known statistical methods and pre-existing knowledge of an object, object properties, such as pose, can be inferred with related uncertainty. This uncertainty is utilized by a grasp planning process which plans for stable grasps under the inferred uncertainty.

Stiffness based trajectory planning and feedforward based vibration suppression control of parallel robot machines

The dissertation proposes two control strategies, which include the trajectory planning and vibration suppression, for a kinematic redundant serial-parallel robot machine, with the aim of attaining the satisfactory machining performance. For a given prescribed trajectory of the robot's end-effector in the Cartesian space, a set of trajectories in the robot's joint space are generated based on the best stiffness performance of the robot along the prescribed trajectory. To construct the required system-wide analytical stiffness model for the serial-parallel robot machine, a variant of the virtual joint method (VJM) is proposed in the dissertation. The modified method is an evolution of Gosselin's lumped model that can account for the deformations of a flexible link in more directions. The effectiveness of this VJM variant is validated by comparing the computed stiffness results of a flexible link with the those of a matrix structural analysis (MSA) method. The comparison shows that the numerical results from both methods on an individual flexible beam are almost identical, which, in some sense, provides mutual validation. The most prominent advantage of the presented VJM variant compared with the MSA method is that it can be applied in a flexible structure system with complicated kinematics formed in terms of flexible serial links and joints. Moreover, by combining the VJM variant and the virtual work principle, a systemwide analytical stiffness model can be easily obtained for mechanisms with both serial kinematics and parallel kinematics. In the dissertation, a system-wide stiffness model of a kinematic redundant serial-parallel robot machine is constructed based on integration of the VJM variant and the virtual work principle. Numerical results of its stiffness performance are reported. For a kinematic redundant robot, to generate a set of feasible joints' trajectories for a prescribed trajectory of its end-effector, its system-wide stiffness performance is taken as the constraint in the joints trajectory planning in the dissertation. For a prescribed location of the end-effector, the robot permits an infinite number of inverse solutions, which consequently yields infinite kinds of stiffness performance. Therefore, a differential evolution (DE) algorithm in which the positions of redundant joints in the kinematics are taken as input variables was employed to search for the best stiffness performance of the robot. Numerical results of the generated joint trajectories are given for a kinematic redundant serial-parallel robot machine, IWR (Intersector Welding/Cutting Robot), when a particular trajectory of its end-effector has been prescribed. The numerical results show that the joint trajectories generated based on the stiffness optimization are feasible for realization in the control system since they are acceptably smooth. The results imply that the stiffness performance of the robot machine deviates smoothly with respect to the kinematic configuration in the adjacent domain of its best stiffness performance. To suppress the vibration of the robot machine due to varying cutting force during the machining process, this dissertation proposed a feedforward control strategy, which is constructed based on the derived inverse dynamics model of target system. The effectiveness of applying such a feedforward control in the vibration suppression has been validated in a parallel manipulator in the software environment. The experimental study of such a feedforward control has also been included in the dissertation. The difficulties of modelling the actual system due to the unknown components in its dynamics is noticed. As a solution, a back propagation (BP) neural network is proposed for identification of the unknown components of the dynamics model of the target system. To train such a BP neural network, a modified Levenberg-Marquardt algorithm that can utilize an experimental input-output data set of the entire dynamic system is introduced in the dissertation. Validation of the BP neural network and the modified Levenberg- Marquardt algorithm is done, respectively, by a sinusoidal output approximation, a second order system parameters estimation, and a friction model estimation of a parallel manipulator, which represent three different application aspects of this method.

Control of Robot by Means of Human Thought

Brain computer interface (BCI) is a kind of human machine interface, which provides a new interaction method between human and computer or other equipment. The most significant characteristic of BCI system is that its control input is brain electrical activities acquired from the brain instead of traditional input such as hands or eyes. BCI technique has rapidly developed during last two decades and it has mainly worked as an auxiliary technique to help the disable people improve their life qualities. With the appearance of low cost novel electrical devices such as EMOTIV, BCI technique has been applied to the general public through many useful applications including video gaming, virtual reality and virtual keyboard. The purpose of this research is to be familiar with EMOTIV EPOC system and make use of it to build an EEG based BCI system for controlling an industrial manipulator by means of human thought. To build a BCI system, an acquisition program based on EMOTIV EPOC system is designed and a MFC based dialog that works as an operation panel is presented. Furthermore, the inverse kinematics of RV-3SB industrial robot was solved. In the last part of this research, the designed BCI system with human thought input is examined and the results indicate that the system is running smoothly and displays clearly the motion type and the incremental displacement of the motion.

Design of traction transmission and suspension systems for an omni-directional mobile robot

This project aims to design and manufacture a mobile robot with two Universal Robot UR10 mainly used indoors. In order to obtain omni-directional maneuverability, the mobile robot is constructed with Mecanum wheels. The Mecanum wheel can move in any direction with a series of rollers attached to itself. These rollers are angled at 45º about the hub’s circumference. This type of wheels can be used in both driving and steering with their any-direction property. This paper is focused on the design of traction system and suspension system, and the velocity control of Mecanum wheels in the close-loop control system. The mechanical design includes selection of bearing housing, couplers which are act as connection between shafts, motor parts, and other needed components. The 3D design software SolidWorks is utilized to assemble all the components in order to get correct tolerance. The driving shaft is designed based on assembled structure via the software as well. The design of suspension system is to compensate the assembly error of Mecanum wheels to guarantee the stability of the robot. The control system of motor drivers is realized through the Robot Operating System (ROS) on Ubuntu Linux. The purpose of inverse kinematics is to obtain the relationship among the movements of all Mecanum wheels. Via programming and interacting with the computer, the robot could move with required speed and direction.