Cytocompatibility of vitreous carbon as a dental implant

Thesis (D.Sc.D.)--Boston University, School of Graduate Dentistry, 1974.Bibliography included

A clinical and experimental study of jet injections

Thesis (M.Sc.D.)--Boston University School of Graduate Dentistry, 1973. Oral Biology.Bibliography included.Since the jet injection was clinically introduced in 1947, little work has been done to evaluate the tissue response to the technique. The present investigation compares the tissue reaction of the jet injections to that of needle injections. The animal investigation was carried out on 30 adult rats, divided into four groups, using a tuberculin syringe with a 26 gauge needle on the control side and a Syrijet Mark II on the experimental. The first group of 18 rats was injected with saline and sacrificed two at a time immediately and at one, two, three, four, six, twelve, twenty-four and forty-eight hours. The second group of eight rats was injected with saline and sacrificed one at a time at two, three, four, six, twelve, twenty-four and forty-eight hours. Prior to sacrifice this group received intraperitoneal injections of trypan blue at half-hourly intervals for a total of three injections and was sacrificed half an hour after the last injection. The third group of two rats received trypan blue injections instead of saline and were sacrificed immediately. The fourth group of two rats was injected with India ink and sacrificed immediately. Stereoscopic and histological examination revealed that injected solutions invariably follow the lines of least resistance in the connective tissues and muscles. Both techniques demonstrate areas of hemorrhage and disruption of connective tissue fibres and displacement of epithelial cells into the underlying connective tissues. An acute inflammatory reaction and damaged connective tissue and muscle fibres was observed following injection techniques. Resolution was rooted in most cases within 48 hours

Structural Optimization of Adaptive Soft Fin Ray Fingers with Variable Stiffening Capability

Soft and adaptable grippers are desired for their ability to operate effectively in unstructured or dynamically changing environments, especially when interacting with delicate or deformable targets. However, utilizing soft bodies often comes at the expense of reduced carrying payload and limited performance in high-force applications. Hence, methods for achieving variable stiffness soft actuators are being investigated to broaden the applications of soft grippers. This paper investigates the structural optimization of adaptive soft fingers based on the Fin Ray® effect (Soft Fin Ray), featuring a passive stiffening mechanism that is enabled via layer jamming between deforming flexible ribs. A finite element model of the proposed Soft Fin Ray structure is developed and experimentally validated, with the aim of enhancing the layer jamming behavior for better grasping performance. The results showed that through structural optimization, initial contact forces before jamming can be minimized and final contact forces after jamming can be significantly enhanced, without downgrading the desired passive adaptation to objects. Thus, applications for Soft Fin Ray fingers can range from adaptive delicate grasping to high-force manipulation tasks

Directly Printable Flexible Strain Sensors for Bending and Contact Feedback of Soft Actuators

This paper presents a fully printable sensorized bending actuator that can be calibrated to provide reliable bending feedback and simple contact detection. A soft bending actuator following a pleated morphology, as well as a flexible resistive strain sensor, were directly 3D printed using easily accessible FDM printer hardware with a dual-extrusion tool head. The flexible sensor was directly welded to the bending actuator’s body and systematically tested to characterize and evaluate its response under variable input pressure. A signal conditioning circuit was developed to enhance the quality of the sensory feedback, and flexible conductive threads were used for wiring. The sensorized actuator’s response was then calibrated using a vision system to convert the sensory readings to real bending angle values. The empirical relationship was derived using linear regression and validated at untrained input conditions to evaluate its accuracy. Furthermore, the sensorized actuator was tested in a constrained setup that prevents bending, to evaluate the potential of using the same sensor for simple contact detection by comparing the constrained and free-bending responses at the same input pressures. The results of this work demonstrated how a dual-extrusion FDM printing process can be tuned to directly print highly customizable flexible strain sensors that were able to provide reliable bending feedback and basic contact detection. The addition of such sensing capability to bending actuators enhances their functionality and reliability for applications such as controlled soft grasping, flexible wearables, and haptic devices

Data-driven bending angle prediction of soft pneumatic actuators with embedded flex sensors

In this paper, resistive flex sensors have been embedded at the strain limiting layer of soft pneumatic actuators, in order to provide sensory feedback that can be utilised in predicting their bending angle during actuation. An experimental setup was prepared to test the soft actuators under controllable operating conditions, record the resulting sensory feedback, and synchronise this with the actual bending angles measured using a developed image processing program. Regression analysis and neural networks are two data-driven modelling techniques that were implemented and compared in this study, to evaluate their ability in predicting the bending angle response of the tested soft actuators at different input pressures and testing orientations. This serves as a step towards controlling this class of soft bending actuators, using data-driven empirical models that lifts the need for complex analytical modelling and material characterisation. The aim is to ultimately create a more controllable version of this class of soft pneumatic actuators with embedded sensing capabilities, to act as compliant soft gripper fingers that can be used in applications requiring both a ‘soft touch’ as well as more controllable object manipulation

Bending angle prediction and control of soft pneumatic actuators with embedded flex sensors: a data-driven approach

In this paper, a purely data-driven modelling approach is presented for predicting and controlling the free bending angle response of a typical soft pneumatic actuator (SPA), embedded with a resistive flex sensor. An experimental setup was constructed to test the SPA at different input pressure values and orientations, while recording the resulting feedback from the embedded flex sensor and on-board pressure sensor. A calibrated high speed camera captures image frames during the actuation, which are then analysed using an image processing program to calculate the actual bending angle and synchronise it with the recorded sensory feedback. Empirical models were derived based on the generated experimental data using two common data-driven modelling techniques; regression analysis and artificial neural networks. Both techniques were validated using a new dataset at untrained operating conditions to evaluate their prediction accuracy. Furthermore, the derived empirical model was used as part of a closed-loop PID controller to estimate and control the bending angle of the tested SPA based on the real-time sensory feedback generated. The tuned PID controller allowed the bending SPA to accurately follow stepped and sinusoidal reference signals, even in the presence of pressure leaks in the pneumatic supply. This work demonstrates how purely data-driven models can be effectively used in controlling the bending of SPAs under different operating conditions, avoiding the need for complex analytical modelling and material characterisation. Ultimately, the aim is to create more controllable soft grippers based on such SPAs with embedded sensing capabilities, to be used in applications requiring both a ‘soft touch’ as well as a more controllable object manipulation

Enhancing Grasp Pose Computation in Gripper Workspace Spheres

In this paper, enhancement to the novel grasp planning algorithm based on gripper workspace spheres is presented. Our development requires a registered point cloud of the target from different views, assuming no prior knowledge of the object, nor any of its properties. This work features a new set of metrics for grasp pose candidates evaluation, as well as exploring the impact of high object sampling on grasp success rates. In addition to gripper position sampling, we now perform orientation sampling about the x, y, and z-axes, hence the grasping algorithm no longer require object orientation estimation. Successful experiments have been conducted on a simple jaw gripper (Franka Panda gripper) as well as a complex, high Degree of Freedom (DoF) hand (Allegro hand) as a proof of its versatility. Higher grasp success rates of 76% and 85.5% respectively has been reported by real world experiments

Bending angle prediction and control of soft pneumatic actuators with embedded flex sensors - a data-driven approach

In this paper, a purely data-driven modelling approach is presented for predicting and controlling the free bending angle response of a typical soft pneumatic actuator (SPA), embedded with a resistive flex sensor. An experimental setup was constructed to test the SPA at different input pressure values and orientations, while recording the resulting feedback from the embedded flex sensor and on-board pressure sensor. A calibrated high speed camera captures image frames during the actuation, which are then analysed using an image processing program to calculate the actual bending angle and synchronise it with the recorded sensory feedback. Empirical models were derived based on the generated experimental data using two common data-driven modelling techniques; regression analysis and artificial neural networks. Both techniques were validated using a new dataset at untrained operating conditions to evaluate their prediction accuracy. Furthermore, the derived empirical model was used as part of a closed-loop PID controller to estimate and control the bending angle of the tested SPA based on the real-time sensory feedback generated. The tuned PID controller allowed the bending SPA to accurately follow stepped and sinusoidal reference signals, even in the presence of pressure leaks in the pneumatic supply. This work demonstrates how purely data-driven models can be effectively used in controlling the bending of SPAs under different operating conditions, avoiding the need for complex analytical modelling and material characterisation. Ultimately, the aim is to create more controllable soft grippers based on such SPAs with embedded sensing capabilities, to be used in applications requiring both a ‘soft touch’ as well as a more controllable object manipulation

Grasping Unknown Objects Based on Gripper Workspace Spheres

In this paper, we present a novel grasp planning algorithm for unknown objects given a registered point cloud of the target from different views. The proposed methodology requires no prior knowledge of the object, nor offline learning. In our approach, the gripper kinematic model is used to generate a point cloud of each finger workspace, which is then filled with spheres. At run-time, first the object is segmented, its major axis is computed, in a plane perpendicular to which, the main grasping action is constrained. The object is then uniformly sampled and scanned for various gripper poses that assure at least one object point is located in the workspace of each finger. In addition, collision checks with the object or the table are performed using computationally inexpensive gripper shape approximation. Our methodology is both time efficient (consumes less than 1.5 seconds in average) and versatile. Successful experiments have been conducted on a simple jaw gripper (Franka Panda gripper) as well as a complex, high Degree of Freedom (DoF) hand (Allegro hand)

Soft pneumatic grippers embedded with stretchable electroadhesion

Current soft pneumatic grippers cannot robustly grasp flat materials and flexible objects on curved surfaces without distorting them. Current electroadhesive grippers, on the other hand, are difficult to actively deform to complex shapes to pick up free-form surfaces or objects. An easy-to-implement PneuEA gripper is proposed by the integration of an electroadhesive gripper and a two-fingered soft pneumatic gripper. The electroadhesive gripper was fabricated by segmenting a soft conductive silicon sheet into a two-part electrode design and embedding it in a soft dielectric elastomer. The two-fingered soft pneumatic gripper was manufactured using a standard soft lithography approach. This novel integration has combined the benefits of both the electroadhesive and soft pneumatic grippers. As a result, the proposed PneuEA gripper was not only able to pick-and-place flat and flexible materials such as a porous cloth but also delicate objects such as a light bulb. By combining two soft touch sensors with the electroadhesive, an intelligent and shape-adaptive PneuEA material handling system has been developed. This work is expected to widen the applications of both soft gripper and electroadhesion technologies