Gross motion analysis of fingertip-based within-hand manipulation

Fingertip-based within-hand manipulation, also called precision manipulation, refers to the repositioning of a grasped object within the workspace of a multi-fingered robot hand without breaking or changing the contact type between each fingertip and the object. Given a robot hand architecture and a set of assumed contact models, this paper presents a method to perform a gross motion analysis of its precision manipulation capabilities, regardless of the particularities of the object being manipulated. In particular, the technique allows the composition of the displacement manifold of the grasped object relative to the palm of the robot hand to be determined as well as the displacements that can be controlled—useful for high-level design and classification of hand function. The effects of a fingertip contacting a body in this analysis are modeled as kinematic chains composed of passive and resistant revolute joints; what permits the introduction of a general framework for the definition and classification of non-frictional and frictional contact types. Examples of the application of the proposed method in several architectures of multi-fingered hands with different contact assumptions are discussed; they illustrate how inappropriate contact conditions may lead to uncontrollable displacements of the grasped object

Development of Object-Based Teleoperator Control for Unstructured Applications

For multi-fingered end effectors in unstructured applications, the main issues are control in the presence of uncertainties and providing grasp stability and object manipulability. The suggested concept in this thesis is object based teleoperator control which provides an intuitive way to control the robot in terms of the grasped object and reduces the operator\u27s conceptual constraints. The general control law is developed using a hierarchical control structure, i.e., human interface I gross motion control level in teleoperation control and fine motion control/object grasp stability in autonomous control. The gross motion control is required to provide the position/orientation of the Super Object (SO), and the sufficient grasping force to the fine motion control. Impedance control is applied to the gross motion control to respond to the environmental forces. The fine motion control consists of serially connecting the finger in position control and the Fingertip Actuation System (FAS) in force control. The FAS has a higher bandwidth response than does the finger actuation system and operates near the center of its joint range. The finger motion controller attempts not only to track the displacement of the FAS but also to provide an FAS centering action. Simulation experiments in both gross and fine motion control are performed. The integrated gross / flue motion control is implemented using the planar configuration of PUMA 560. The results show that the desired contact force can be maintained in the direction of FAS motion. The mathematical proof of system stability and the extension to spatial systems are required to complete the research

In-hand manipulation planning using human motion dictionary

Dexterous in-hand manipulation is a peculiar and useful human skill. This ability requires the coordination of many senses and hand motion to adhere to many constraints. These constraints vary and can be influenced by the object characteristics or the specific application. One of the key elements for a robotic platform to implement reliable inhand manipulation skills is to be able to integrate those constraints in their motion generations. These constraints can be implicitly modelled, learned through experience or human demonstrations. We propose a method based on motion primitives dictionaries to learn and reproduce in-hand manipulation skills. In particular, we focused on fingertip motions during the manipulation, and we defined an optimization process to combine motion primitives to reach specific fingertip configurations. The results of this work show that the proposed approach can generate manipulation motion coherent with the human one and that manipulation constraints are inherited even without an explicit formalization

Haptic Exploration of Unknown Objects for Robust in-hand Manipulation.

Human-like robot hands provide the flexibility to manipulate a variety of objects that are found in unstructured environments. Knowledge of object properties and motion trajectory is required, but often not available in real-world manipulation tasks. Although it is possible to grasp and manipulate unknown objects, an uninformed grasp leads to inferior stability, accuracy, and repeatability of the manipulation. Therefore, a central challenge of in-hand manipulation in unstructured environments is to acquire this information safely and efficiently. We propose an in-hand manipulation framework that does not assume any prior information about the object and the motion, but instead extracts the object properties through a novel haptic exploration procedure and learns the motion from demonstration using dynamical movement primitives. We evaluate our approach by unknown object manipulation experiments using a human-like robot hand. The results show that haptic exploration improves the manipulation robustness and accuracy significantly, compared to the virtual spring framework baseline method that is widely used for grasping unknown objects

A survey of dextrous manipulation

technical reportThe development of mechanical end effectors capable of dextrous manipulation is a rapidly growing and quite successful field of research. It has in some sense put the focus on control issues, in particular, how to control these remarkably humanlike manipulators to perform the deft movement that we take for granted in the human hand. The kinematic and control issues surrounding manipulation research are clouded by more basic concerns such as: what is the goal of a manipulation system, is the anthropomorphic or functional design methodology appropriate, and to what degree does the control of the manipulator depend on other sensory systems. This paper examines the potential of creating a general purpose, anthropomorphically motivated, dextrous manipulation system. The discussion will focus on features of the human hand that permit its general usefulness as a manipulator. A survey of machinery designed to emulate these capabilities is presented. Finally, the tasks of grasping and manipulation are examined from the control standpoint to suggest a control paradigm which is descriptive, yet flexible and computationally efficient1

Surface Electromyographic (sEMG) Transduction of Hand Joint Angles for Human Interfacing Devices (HID)

This is an investigation of the use of surface electromyography (sEMG) as a tool to improve human interfacing devices (HID) information bandwidth through the transduction of the fingertip workspace. It combines the work of Merletti et al and Jarque-Bou et al to design an open-source framework for Fingertip Workspace based Human Interfacing Devices (HID). In this framework, the fingertip workspace is defined as the system of forearm and hand muscle force through a tensor which describes hand anthropometry. The thesis discusses the electrophysiology of muscle tissue along with the anatomy and physiology of the arm in pursuit of optimizing sensor location, muscle force measurements, and viable command gestures. Algorithms for correlating sEMG to hand joint angle are investigated using MATLAB for both static and moving gestures. Seven sEMG spots and Fingertip Joint Angles recorded by Jarque Bou et al are investigated for the application of sEMG to Human Interfacing Devices (HID). Such technology is termed Gesture Computer Interfacing (GCI) and has been shown feasible through devices such as CTRL Labs interface, and models such as those of Sartori, Merletti, and Zhao. Muscles under sEMG spots in this dataset and the actions related to them are discussed, along with what muscles and hand actions are not visible within this dataset. Viable gestures for detection algorithms are discussed based on the muscles discerned to be visible in the dataset through intensity, spectral moment, power spectra, and coherence. Detection and isolation of such viable actions is fundamental to designing an EMG driven musculoskeletal model of the hand needed to facilitate GCI. Enveloping, spectral moment, power spectrum, and coherence analysis are applied to a Sollerman Hand Function Test sEMG dataset of twenty-two subjects performing 26 activities of living to differentiate pinching and grasping tasks. Pinches and grasps were found to cause very different activation patterns in sEMG spot 3 relating to flexion of digits I - V. Spectral moment was found to be less correlated with differentiation and provided information about the degree of object manipulation performed and extent of fatigue during each task. Coherence was shown to increase between flexors and extensors with intensity of task but was found corrupted by crosstalk with increasing intensity of muscular activation. Some spectral results correlated between finger flexor and extensor power spectra showed anticipatory coherence between the muscle groups at the end of object manipulation. An sEMG amplification system capable of capturing HD-sEMG with a bandwidth of 300 and 500 Hz at a sampling frequency of 2 kHz was designed for future work. The system was designed in ordinance with current IEEE research on sensor-electrode characteristics. Furthermore, discussion of solutions to open issues in HD-sEMG is provided. This work did not implement the designed wristband but serves as a literature review and open-source design using commercially available technologies