Replicating human hand synergies onto robotic hands: a review on software and hardware strategies

This review reports the principal solutions proposed in the literature to reduce the complexity of the control and of the design of robotic hands taking inspiration from the organization of the human brain. Several studies in neuroscience concerning the sensorimotor organization of the human hand proved that, despite the complexity of the hand, a few parameters can describe most of the variance in the patterns of configurations and movements. In other words, humans exploit a reduced set of parameters, known in the literature as synergies, to control their hands. In robotics, this dimensionality reduction can be achieved by coupling some of the degrees of freedom (DoFs) of the robotic hand, that results in a reduction of the needed inputs. Such coupling can be obtained at the software level, exploiting mapping algorithm to reproduce human hand organization, and at the hardware level, through either rigid or compliant physical couplings between the joints of the robotic hand. This paper reviews the main solutions proposed for both the approaches

An Object-Based Approach to Map Human Hand Synergies onto Robotic Hands with Dissimilar Kinematics

Robotic hands differ in kinematics, dynamics, programming, control and sensing frameworks. Borrowing the terminology from software engineering, there is a need for middleware solutions to control the robotic hands independently from their specific structure, and focusing only on the task. Results in neuroscience concerning the synergistic organization of the human hand, are the theoretical foundation of this work, which focuses on the problem of mapping human hand synergies on robotic hands with dissimilar kinematic structures. The proposed mapping is based on the use of a virtual ellipsoid and it is mediated by a model of an anthropomorphic robotic hand able to capture the idea of synergies in human hands. This approach has been tested in two different robotic hands with an anthropomorphic and non-anthropomorphic kinematic structure

Data-driven Mechanical Design and Control Method of Dexterous Upper-Limb Prosthesis

With an increasing number of people, 320,000 per year, suffering from impaired upper limb function due to various medical conditions like stroke and blunt trauma, the demand for highly functional upper limb prostheses is increasing; however, the rates of rejection of prostheses are high due to factors such as lack of functionality, high cost, weight, and lack of sensory feedback. Modern robotics has led to the development of more affordable and dexterous upper limb prostheses with mostly anthropomorphic designs. However, due to the highly sophisticated ergonomics of anthropomorphic hands, most are economically prohibitive and suffer from control complexity due to increased cognitive load on the user. Thus, this thesis work aims to design a prosthesis that relies on the emulation of the kinematics and contact forces involved in grasping tasks with healthy human hands rather than on biomimicry for reduction of mechanical complexity and utilization of technologically advanced engineering components. This is accomplished by 1) experimentally characterizing human grasp kinematics and kinetics as a basis for data-driven prosthesis design. Using the grasp data, steps are taken to 2) develop a data-driven design and control method of an upper limb prosthesis that shares the kinematics and kinetics required for healthy human grasps without taking the anthropomorphic design. This thesis demonstrates an approach to decrease the gap between the functionality of the human hand and robotic upper limb prostheses by introducing a method to optimize the design and control method of an upper limb prosthesis. This is accomplished by first, collecting grasp data from human subjects with a motion and force capture glove. The collected data are used to minimize control complexity by reducing the dimensionality of the device while fulfilling the kinematic and kinetic requirements of daily grasping tasks. Using these techniques, a task-oriented upper limb prosthesis is prototyped and tested in simulation and physical environment.Ph.D