Planning dextrous robot hand grasps from range data, using preshapes and digit trajectories

Dextrous robot hands have many degrees of freedom. This enables the manipulation of objects between the digits of the dextrous hand but makes grasp planning substantially more complex than for parallel jaw grippers. Much of the work that addresses grasp planning for dextrous hands concentrates on the selection of contact sites to optimise stability criteria and ignores the kinematics of the hand. In more complete systems, the paradigm of preshaping has emerged as dominant. However, the criteria for the formation and placement of the preshapes have not been adequately examined, and the usefulness of the systems is therefore limited to grasping simple objects for which preshapes can be formed using coarse heuristics.In this thesis a grasp metric based on stability and kinematic feasibility is introduced. The preshaping paradigm is extended to include consideration of the trajectories that the digits take during closure from preshape to final grasp. The resulting grasp family is dependent upon task requirements and is designed for a set of "ideal" object-hand configurations. The grasp family couples the degrees of freedom of the dextrous hand in an anthropomorphic manner; the resulting reduction in freedom makes the grasp planning less complex. Grasp families are fitted to real objects by optimisation of the grasp metric; this corresponds to fitting the real object-hand configuration as close to the ideal as possible. First, the preshape aperture, which defines the positions of the fingertips in the preshape, is found by optimisation of an approximation to the grasp metric (which makes simplifying assumptions about the digit trajectories and hand kinematics). Second, the full preshape kinematics and digit closure trajectories are calculated to optimise the full grasp metric.Grasps are planned on object models built from laser striper range data from two viewpoints. A surface description of the object is used to prune the space of possible contact sites and to allow the accurate estimation of normals, which is required by the grasp metric to estimate the amount of friction required. A voxel description, built by ray-casting, is used to check for collisions between the object and the robot hand using an approximation to the Euclidean distance transform.Results are shown in simulation for a 3-digit hand model, designed to be like a simplified human hand in terms of its size and functionality. There are clear extensions of the method to any dextrous hand with a single thumb opposing multiple fingers and several different hand models that could be used are described. Grasps are planned on a wide variety of curved and polyhedral object

A robotic engine assembly pick-place system based on machine learning

Industrial revolution brought humans and machines together in building a better future. Where in one hand there is need to replace the repetitive jobs with machines to increase efficiency and volume of production, on the other hand intelligent and autonomous machines have still a long way to go to achieve dexterity of a human. The current scenario requires a system which can utilise best of both the human and the machine. This thesis studies a industrial use case scenario where human-machine combine their skills to build an autonomous pick place system. This study takes a small step towards the human-robot consortium primarily focusing on developing a vision based system for object detection followed by a manipulator pick place operation. This thesis can be divided into two parts : 1. Scene analysis, where a Convolutional Neural Network (CNN) is used for object detection followed by generation of grasping points using object edge image and an algorithm developed during this thesis. 2. Implementation, it focuses on motion generation while taking care of external disturbances to perform successful pick-place operation. In addition human involvement is required which includes teaching trajectory points for the robot to follow. This trajectory is used to generate image data-set for a new object type and thereafter generating new object detection model. The author primarily focuses on building a system framework where the complexities related to robot programming such as generating trajectory points and informing grasping position is not required. The system automatically detects object and performs a pick place operation, resulting in relieving user from robot programming. The system is composed of a depth camera and a manipulator. Camera is the only sensor available for scene analysis and the action is performed using a Franka manipulator. The two components work in request-response mode over ROS. This thesis introduces a newer approaches such as, dividing an workspace image into its constituent object images and performing object detection, creating training data, generating grasp points based on object shape along length of an object. The thesis also presents a case study where three different objects are chosen as test objects. The experiments are a demonstration of the methods applied and efficiency attained. The case study also provides a glimpse of the future research and development areas

Design, development and evaluation of Stanford/Ames Extra-Vehicular Activity (EVA) prehensors

A summary is given of progress to date on work proposed in 1983 and continued in 1985, including design iterations on three different types of manually powered prehensors, construction of functional mockups of each and culminating in detailed drawings and specifications for suit-compatible sealed units for testing under realistic conditions

Haptic Perception with a Robot Hand: Requirements and Realization

This paper first discusses briefly some of the recent ideas of perceptual psychology on the human haptic system particularly those of J.J. Gibson and Klatzky and Lederman. Following this introduction, we present some of the requirements of robotic haptic sensing and the results of experiments using a Utah/MIT dexterous robot hand to derive geometric object information using active sensing

Advancing the Underactuated Grasping Capabilities of Single Actuator Prosthetic Hands

The last decade has seen significant advancements in upper limb prosthetics, specifically in the myoelectric control and powered prosthetic hand fields, leading to more active and social lifestyles for the upper limb amputee community. Notwithstanding the improvements in complexity and control of myoelectric prosthetic hands, grasping still remains one of the greatest challenges in robotics. Upper-limb amputees continue to prefer more antiquated body-powered or powered hook terminal devices that are favored for their control simplicity, lightweight and low cost; however, these devices are nominally unsightly and lack in grasp variety. The varying drawbacks of both complex myoelectric and simple body-powered devices have led to low adoption rates for all upper limb prostheses by amputees, which includes 35% pediatric and 23% adult rejection for complex devices and 45% pediatric and 26% adult rejection for body-powered devices [1]. My research focuses on progressing the grasping capabilities of prosthetic hands driven by simple control and a single motor, to combine the dexterous functionality of the more complex hands with the intuitive control of the more simplistic body-powered devices with the goal of helping upper limb amputees return to more active and social lifestyles. Optimization of a prosthetic hand driven by a single actuator requires the optimization of many facets of the hand. This includes optimization of the finger kinematics, underactuated mechanisms, geometry, materials and performance when completing activities of daily living. In my dissertation, I will present chapters dedicated to improving these subsystems of single actuator prosthetic hands to better replicate human hand function from simple control. First, I will present a framework created to optimize precision grasping – which is nominally unstable in underactuated configurations – from a single actuator. I will then present several novel mechanisms that allow a single actuator to map to higher degree of freedom motion and multiple commonly used grasp types. I will then discuss how fingerpad geometry and materials can better grasp acquisition and frictional properties within the hand while also providing a method of fabricating lightweight custom prostheses. Last, I will analyze the results of several human subject testing studies to evaluate the optimized hands performance on activities of daily living and compared to other commercially available prosthesis