Nature grasping by a cable-driven under-actuated anthropomorphic robotic hand

Human hand is the best sample for humanoid robotic hand and a nature grasping is the final target that most robotic hands are pursuing. Many prior researches had been done in virtual and real for simulation the human grasping. Unfortunately, there is no perfect solution to duplicate the nature grasping of human. The main difficulty comes from three points. 1. How to 3D modelling and fabricate the real hand. 2. How actuated the robotic hand as real hand. 3. How to grasp objects in different shapes like human hand. To deal with these three problems and further to provide a partial solution for duplicate human grasping, this paper introduces our method to solve these problems from robotic hand design, fabrication, actuation and grasping plan. Our modelling progress takes only around 12 minutes that include 10 minutes of 3D scanning of a real human hand and two minutes for changing the scanned model to an articulated model by running our algorithm. Our grasping plan is based on the sampled trajectory and easy to implement for grasping different objects. Followed these steps, a seven DOF robotic hand is created and tested in the experiments

Design and Exploration of Feedforward Haptic Feedback in Anthropomorphically-Driven Prostheses

Here, we present a wearable, anthropomorphically-driven prosthesis with a built-in haptic feedback system. The device was designed and built to accommodate specific design parameters. Two control schemes were proposed and compared in a user study with N=6 able-bodied participants performing the Box and Blocks test. The first control scheme was designed to provide an intuitive, human-like actuation and relaxation of the hand, while the other controller was designed to reduce fatigue from sustaining EMG signals. Participants performed significantly better with lower fatigue levels while using the intuitive controller as opposed to the second controller. In addition, task performance with both controllers was better than reported performance with standard myoelectric prostheses. In addition, a second experiment compared the unilateral manual dexterity of N=3 able-bodied participants under three distinct conditions: vibration haptic feedback, skin stretch haptic feedback, and no haptic feedback. These findings suggest that there is utility in wearable anthropomorphically-driven prostheses, and provide support for future studies aimed at exploring anthropomorphically-driven prostheses

Design and implementation of an anthropomorphic hand for replicating human grasping functions

How to design an anthropomorphic hand with a few actuators to replicate the grasping functions of the human hand is still a challenging problem. This paper aims to develop a general theory for designing the anthropomorphic hand and endowing the designed hand with natural grasping functions. A grasping experimental paradigm was set up for analyzing the grasping mechanism of the human hand in daily living. The movement relationship among joints in a digit, among digits in the human hand, and the postural synergic characteristic of the fingers were studied during the grasping. The design principle of the anthropomorphic mechanical digit that can reproduce the digit grasping movement of the human hand was developed. The design theory of the kinematic transmission mechanism that can be embedded into the palm of the anthropomorphic hand to reproduce the postural synergic characteristic of the fingers by using a limited number of actuators is proposed. The design method of the anthropomorphic hand for replicating human grasping functions was formulated. Grasping experiments are given to verify the effectiveness of the proposed design method of the anthropomorphic hand. © 2016 IEEE

Conception, analyse et optimisation de méthodes de préhension et de mains mécaniques épicycloïdales pour la prise d'objets plats partiellement contraints

Dans les applications robotiques, la plupart des préhenseurs sont plus apparentés à des outils qui sont spécialisés pour effectuer une tâche extrêmement bien plutôt que d’effectuer une variété de tâches et de simplement les réussir. C’est dans cette optique que les travaux rapportés dans cette thèse proposent des solutions de préhension. Premièrement, des méthodes générales sont proposées pour permettre de prendre un type d’objets qui est généralement impossible à prendre pour les préhenseurs simples. Par la suite sont présentés les mécanismes planétaires qui sont au cœur des assemblages subséquents. Ces mécanismes sont utilisés pour améliorer les débattements des doigts et ainsi rendent possible un premier design pouvant prendre des petits et grands objets reposant sur des surfaces dures. Par la suite est présenté la conception d’un préhenseur complet qui inclut les propriétés du premier préhenseur mais aussi des propriétés de prises parallèles qui sont considérées comme indispensables pour être en mesure de saisir une grande panoplie d’objets. Finalement, le design du préhenseur proposé est optimisé et des capteurs y sont intégrés pour tenter de produire un design complet et sécuritaire pouvant être utilisé de manière simple par une grande panoplie de robots.Most robotic grippers excel at completing one task but are ill suited for completing many and very different tasks. It is with this fact in mind that this thesis proposes general solutions to the grasping problem. First, general methods are proposed that aim at picking small flat objects that could not otherwise be grasped by simple mechanical grippers. Planetary mechanisms are then proposed to increase the range of motion of the finger joints, hence providing a way to achieve the necessary properties to build and test a finger capable of grasping small flat objects lying on hard surfaces. A complete gripper design is then proposed and built. The novel design that includes the features of the previous design is also capable of performing parallel grasps which are considered essential to be able to grasp a wide range of unknown objects. Finally, the gripper design is optimised and sensing apparatus is included in the gripper to provide a gripper that is considered a complete solution to grasping and is simple to use on a wide range of robots

Learning-based robotic manipulation for dynamic object handling : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Mechatronic Engineering at the School of Food and Advanced Technology, Massey University, Turitea Campus, Palmerston North, New Zealand

Figures are re-used in this thesis with permission of their respective publishers or under a Creative Commons licence.Recent trends have shown that the lifecycles and production volumes of modern products are shortening. Consequently, many manufacturers subject to frequent change prefer flexible and reconfigurable production systems. Such schemes are often achieved by means of manual assembly, as conventional automated systems are perceived as lacking flexibility. Production lines that incorporate human workers are particularly common within consumer electronics and small appliances. Artificial intelligence (AI) is a possible avenue to achieve smart robotic automation in this context. In this research it is argued that a robust, autonomous object handling process plays a crucial role in future manufacturing systems that incorporate robotics—key to further closing the gap between manual and fully automated production. Novel object grasping is a difficult task, confounded by many factors including object geometry, weight distribution, friction coefficients and deformation characteristics. Sensing and actuation accuracy can also significantly impact manipulation quality. Another challenge is understanding the relationship between these factors, a specific grasping strategy, the robotic arm and the employed end-effector. Manipulation has been a central research topic within robotics for many years. Some works focus on design, i.e. specifying a gripper-object interface such that the effects of imprecise gripper placement and other confounding control-related factors are mitigated. Many universal robotic gripper designs have been considered, including 3-fingered gripper designs, anthropomorphic grippers, granular jamming end-effectors and underactuated mechanisms. While such approaches have maintained some interest, contemporary works predominantly utilise machine learning in conjunction with imaging technologies and generic force-closure end-effectors. Neural networks that utilise supervised and unsupervised learning schemes with an RGB or RGB-D input make up the bulk of publications within this field. Though many solutions have been studied, automatically generating a robust grasp configuration for objects not known a priori, remains an open-ended problem. An element of this issue relates to a lack of objective performance metrics to quantify the effectiveness of a solution—which has traditionally driven the direction of community focus by highlighting gaps in the state-of-the-art. This research employs monocular vision and deep learning to generate—and select from—a set of hypothesis grasps. A significant portion of this research relates to the process by which a final grasp is selected. Grasp synthesis is achieved by sampling the workspace using convolutional neural networks trained to recognise prospective grasp areas. Each potential pose is evaluated by the proposed method in conjunction with other input modalities—such as load-cells and an alternate perspective. To overcome human bias and build upon traditional metrics, scores are established to objectively quantify the quality of an executed grasp trial. Learning frameworks that aim to maximise for these scores are employed in the selection process to improve performance. The proposed methodology and associated metrics are empirically evaluated. A physical prototype system was constructed, employing a Dobot Magician robotic manipulator, vision enclosure, imaging system, conveyor, sensing unit and control system. Over 4,000 trials were conducted utilising 100 objects. Experimentation showed that robotic manipulation quality could be improved by 10.3% when selecting to optimise for the proposed metrics—quantified by a metric related to translational error. Trials further demonstrated a grasp success rate of 99.3% for known objects and 98.9% for objects for which a priori information is unavailable. For unknown objects, this equated to an improvement of approximately 10% relative to other similar methodologies in literature. A 5.3% reduction in grasp rate was observed when removing the metrics as selection criteria for the prototype system. The system operated at approximately 1 Hz when contemporary hardware was employed. Experimentation demonstrated that selecting a grasp pose based on the proposed metrics improved grasp rates by up to 4.6% for known objects and 2.5% for unknown objects—compared to selecting for grasp rate alone. This project was sponsored by the Richard and Mary Earle Technology Trust, the Ken and Elizabeth Powell Bursary and the Massey University Foundation. Without the financial support provided by these entities, it would not have been possible to construct the physical robotic system used for testing and experimentation. This research adds to the field of robotic manipulation, contributing to topics on grasp-induced error analysis, post-grasp error minimisation, grasp synthesis framework design and general grasp synthesis. Three journal publications and one IEEE Xplore paper have been published as a result of this research