Browsing by Subject "Grasping"

Now showing 1 - 3 of 3

Effects of passive parallel compliance in tendon-driven robotic hands
(2013-12) Niehues, Taylor D.; Deshpande, Ashok D.
Humans utilize the inherent biomechanical compliance present in their fingers for increased stability and dexterity during manipulation tasks. While series elastic actuation has been explored, little research has been performed on the role of joint compliance arranged in parallel with the actuators. The goal of this thesis is to demonstrate, through simulation studies and experimental analyses, the advantages gained by employing human-like passive compliance in finger joints when grasping. We first model two planar systems: a single 2-DOF (degree of freedom) finger and a pair of 2-DOF fingers grasping an object. In each case, combinations of passive joint compliance and active stiffness control are implemented, and the impulse disturbance responses are compared. The control is carried out at a limited sampling frequency, and an energy analysis is performed to investigate stability. Our approach reveals that limited controller frequency leads to increased actuator energy input and hence a less stable system, and human-like passive parallel compliance can improve stability and robustness during grasping tasks. Then, an experimental setup is designed consisting of dual 2-DOF tendon-driven fingers. An impedance control law for two-fingered object manipulation is developed, using a novel friction compensation technique for improved actuator force control. This is used to experimentally quantify the advantages of parallel compliance during dexterous manipulation tasks, demonstrating smoother trajectory tracking and improved stability and robustness to impacts.
Quantifying grasp quality using an inverse reinforcement learning algorithm
(2017-05) Horn, Matthew William; Landsberger, Sheldon; Pryor, Mitchell Wayne
This thesis considers the problem of using a learning algorithm to recognize when a mechanical gripper and sensor combination has achieved a robust grasp. Robotic hands are continuously evolving with finer motor control and higher degrees of freedom which can complicate the ability of an operator to determine if a gripper has achieved a successful grasp. Robots working in hazardous environments especially need confirmation of a successful grasp as the cost of failure is often higher than in traditional factory environments. The object set found in a nuclear environment is the focus of this effort. Objects in this environment are typically expensive (or one-of-a-kind), rigid, radioactive (or toxic), dense, and susceptible to dents, scratches, and oxidation. To validate the robustness of a grasp option, an online inverse reinforcement learning approach is evaluated as a method to quantify grasp quality. This approach is applied to an industrial-grade under-actuated robotic hand equipped with 36 pressure sensors. An expert trains the inverse reinforcement learning algorithm to generate a reward function which scores each grasp so - when combined with fuzzy logic - provides a general success or fail along with a confidence level. Utilizing the trained inverse reinforcement learning algorithm in a glovebox environment reduces the number of potential failing and untrustworthy grasps by scoring executed grasps and rejecting grasps that are similar to prior failed grasps while allowing further execution of movement when a grasp has been scored highly. The trained algorithm incorrectly classified grasps of insufficient quality less than 5% of the time in experimental hardware tests, showing that the algorithm can be applied to the glovebox environment to improve grasp safety. Thus the combination of grasp selection and pressure sensor validation provides a more efficient, robust, and redundant method to assure items can be safely handled during remote automation processes.
A shape primitive-based grasping strategy using visual object recognition in confined, hazardous environments
(2013-12) Brabec, Cheryl Lynn; Landsberger, Sheldon; Pryor, Mitchell Wayne
Grasping can be a complicated process for robotics due to the replication of human fine motor skills and typically high degrees of freedom in robotic hands. Robotic hands that are underactuated provide a method by which grasps can be executed without the onerous task of calculating every fingertip placement. The general shape configuration modes available to underactuated hands lend themselves well to an approach of grasping by shape primitives, and especially so when applied to gloveboxes in the nuclear domain due to the finite number of objects anticipated and the safe assumption that objects in the set are rigid. Thus, the object set found in a glovebox can be categorized as a small set of primitives such as cylinders, cubes, and bowls/hemispheres, etc. These same assumptions can also be leveraged for reliable identification and pose estimation within a glovebox. This effort develops and simulates a simple, but robust and effective grasp planning algorithm for a 7DOF industrial robot and three fingered dexterous, but underactuated robotic hand. The proposed grasping algorithm creates a grasp by generating a vector to the object from the base of the robot and manipulating that vector to be in a suitable starting location for a grasp. The grasp preshapes are selected to match shape primitives and are built-in to the Robotiq gripper used for algorithm demonstration purposes. If a grasp is found to be unsuitable via an inverse kinematics solution check, the algorithm procedurally generates additional grasps to try based on object geometry until a solution can be found or all possibilities are exhausted. The algorithm was tested and found capable of generating valid grasps for visually identified objects, and can recalculate grasps if one is found to be incompatible with the current kinematics of the robotic arm.