Dissertations / Theses on the topic 'Robotics hand'

A better understanding of human hand functionality can help improve robotic and prosthetic hand capability, as well as having benefits for rehabilitation or device design. While the human hand has been studied extensively in various fields, fewer existing works study the human hand within frameworks which can be easily applied to robotic applications, or attempt to quantify complex human hand functionality in real-world environments or with tasks approaching real-world complexity. This dissertation presents a study of human hand functionality from the multiple angles of high level classification methods, whole-hand grasp usage, and precision manipulation, where a small object is repositioned in the fingertips.

Our manipulation classification work presents a motion-centric scheme which can be applied to any human or hand-based robotic manipulation task. Most previous classifications are domain specific and cannot easily be applied to both robotic and human tasks, or can only be applied to a certain subset of manipulation tasks. We present a number of criteria which can be used to describe manipulation tasks and understand differences in the hand functionality used. These criteria are then applied to a number of real world example tasks, including a description of how the classification state can change over time during a dynamic manipulation task.

Next, our study of real-world grasping contributes to an understanding of whole-hand usage. Using head mounted camera video from two housekeepers and two machinists, we analyze the grasps used in their natural work environments. By tagging both grasp state and objects involved, we can measure the prevalence of each grasp and also understand how the grasp is typically used. We then use the grasp-object relationships to select small sets of versatile grasps which can still handle a wide variety of objects, which are promising candidates for implementation in robotic or prosthetic manipulators.

Following the discussion of overall hand shapes, we then present a study of precision manipulation, or how people reposition small objects in the fingertips. Little prior work was found which experimentally measures human capabilities with a full multi-finger precision manipulation task. Our work reports the size and shape for the precision manipulation workspace, and finds that the overall workspace is small, but also has a certain axis along which more object movement is possible. We then show the effect of object size and the number of fingers used on the resulting workspace volume – an ideal object size range is determined, and it is shown that adding additional fingers will reduce workspace volume, likely due to the additional kinematic constraints. Using similar methods to our main precision manipulation investigation, but with a spherical object rolled in the fingertips, we also report the overall fingertip surface usage for two- and three-fingered manipulation, and show a shift in typical fingertip area used between the two and three finger cases.

The experimental precision manipulation data is then used to refine the design of an anthropomorphic precision manipulator. The human precision manipulation workspace is used to select suitable spring ratios for the robotic fingers, and the resulting hand is shown to achieve about half of the average human workspace, despite using only three actuators.

Overall, we investigate multiple aspects of human hand function, as well as constructing a new framework for analyzing human and robotic manipulation. This work contributes to an improved understanding of human grasp usage in real-world environments, as well as human precision manipulation workspace. We provide a demonstration of how some of the studied aspects of human hand function can be applied to anthropomorphic manipulator design, but we anticipate that the results will also be of interest in other fields, such as by helping to design devices matched to hand capabilities and typical usage, or providing inspiration for future methods to rehabilitate hand function.

My thesis covers the design and fabrication of novel humanoid robotic eyes and the process of interfacing them with the industry robot, Baxter. The mechanism can reach a maximum saccade velocity comparable to that of human eyes. Unlike current robotic eye designs, these eyes have independent left-right and up-down gaze movements achieved using a servo and DC motor, respectively. A potentiometer and rotary encoder enable closed-loop control. An Arduino board and motor driver control the assembly. The motor requires a 12V power source, and all other components are powered through the Arduino from a PC.

Hand-eye coordination research influenced how the eyes were programmed to move relative to Baxter’s grippers. Different modes were coded to adjust eye movement based on the durability of what Baxter is handling. Tests were performed on a component level as well as on the full assembly to prove functionality.

Medical science has made it possible to use prosthetic devices to restore the basic abilities needed to function in everyday life. Although robotic prosthetic hands can improve mobility over a simple hook prosthetic, the current state-of-the-art devices are still limited in their ability to grasp and hold objects as quickly and as accurately as the natural human hand. This project trains a deep learning neural network to control a robotic prosthetic hand in performing a grasping task.

In their daily lives, stroke survivors must often choose between attempting upper-extremity activities using their impaired limb, or compensating with their less impaired limb. Choosing their impaired limb can be difficult and discouraging, but might elicit beneficial neuroplasticity that further reduces motor impairments, a phenomenon referred to as “the virtuous cycle”. In contrast, compensation is often quicker, easier, and more effective, but can reinforce maladaptive changes that limit motor recovery, a phenomenon referred to as “learned non-use”. This dissertation evaluated the role of robotic assistance in, and designed a wearable sensing system for, promoting the virtuous cycle.

In the first half of the dissertation, we use the FINGER robot to test the hypothesis that robotic assistance during clinical movement training triggers the virtual cycle. FINGER consists of two singly-actuated mechanisms that assist individuated movement of the index and middle fingers. 30 chronic stroke participants trained in FINGER using a GuitarHero-like game for nine sessions. Half were guided by an adaptive impedance controller towards a success rate of 85%, while the other half were guided towards 50%. Increasing assistance to enable successful practice decreased effort, but primarily for less-impaired participants. Overall, however, high success practice was as effective (or more) as low success practice and even more effective for highly impaired individuals. Participants who received high assistance training were more motivated and reported using their impaired hand more at home. These results support the hypothesis that high assistance clinical movement training motivates impaired hand use, leading to greater use of the hand in daily life, resulting in a self-training effect that reduces motor impairment.

The second half of the dissertation describes the development of the manumeter - a non-obtrusive wearable device for monitoring and incentivizing impaired hand use. Contrasted against wrist accelerometry (the most comparable technology), the manumeter uses a magnetic ring and a wristband with mangetometers to detect wrist and finger movement rather than gross arm movement. We describe 1) the inference of wrist and finger movement from differential magnetometer readings using a radial basis function network, 2) initial testing in which distance traveled estimates were within 94.7%±19.3 of their goniometricly measured values, 3) experiments with non-impaired participants in which the manumeter detected some functional activities better than wrist accelerometry, and 4) improvements to the hardware and data processing that allow both subject-independent tracking of the position of the finger relative to the wrist (RMS errors < 1cm) and highly reliable detection of whether the hand is open or closed. Its performance and non-obtrusive design make the manumeter well suited for measuring and reinforcing impaired hand use in daily life after stroke.

The contributions of this dissertation are experimental confirmation that high assistance movement training promotes the virtuous cycle, and development of a wearable sensor for monitoring hand movement in daily life. Training with robotic assistance and hand use feedback may ultimately help individuals with stroke recover to their full potential.

Astronaut hand fatigue during Extravehicular Activity (EVA) and EVA training is a critical risk in human space exploration. Improved glove designs over the past forty years have reduced hand fatigue, but limitations of the technology prevent major improvements to reduce hand fatigue. Therefore, a mechanism to assist astronauts by reducing hand fatigue was explored. Many organizations have already developed exoskeletons to assist astronauts, but all mechanisms developed required electrically powered actuators and control systems to enhance grip strength. However, astronauts already possess the strength required to actuate the glove; what is needed is a method to reduce fatigue without introducing electromechanical complexity. A passive mechanical system was developed as a proof-of-concept to test the feasibility of an unpowered exoskeleton to maintain static grip around an object. The semi- rigid nature of an inflated pressure glove provided an ideal substrate to mount a mechanism and associated components to allow an astronaut to release his/her grip inside the glove while maintaining attitude, as the mechanism will keep the glove closed around an object.

Three prototypes were fabricated and tested to evaluate the architecture. The final two prototypes were tested on a real pressure suit glove at Final Frontier Design (FFD), and the third mechanism demonstrated attachment and basic operating principles. At University of California (UC) Davis, pressure glove analogs were fabricated from a baseball batting glove and polystyrene to simulate a real pressure glove without the risk of testing in a reduced pressure environment (i.e. a glove box). Testing of the third prototype showed a reduction in fatigue as measured by Maximum Voluntary Contraction (MVC) grip force over a 30 second period when the mechanism assisted gripping an object.

Aims and Hypothesis: The objective of this study was the design and testing of a Prototype Computer Numerical Control (CNC) dental handpiece. We predicted that the CNC Prototype would be more accurate than the human participant prosthodontists in clinical simulation.

Materials and Methods: A Prototype CNC dental handpiece was developed from off the shelf components, assigned 100 typodont teeth (#18) for submission and 10 practice teeth. Single operator. Five prosthodontists, given 20 typodont teeth (#18) for submission and 10 for practice. Finished preparations were scanned with 3M True Definition^® intraoral scanner outside of typodont, compared with Geomagic Control for RMSE.

Results: RMSE Prototype (N=100) was 0.40mm. RMSE Prosthodontists (N=100) was 0.55mm. One sided T test, mean difference −.15mm (p<.001, one sided CI −.09). One Way ANOVA (F stat <1, F=.526, p=.717), Spearman correlation Prototype RMSE vs order(ρ=.1, p=.334), RMSE vs Bur (ρ=.36, p<.001); For each prosthodontist individually (N=20) RMSE vs Order Prosthodontist 4(ρ=−.54, p= .015). Prosthodontist 5 (ρ= .58, p = .022). Prosthodontist 3 (ρ=.16, p=.498), Prosthodontist 2 (ρ=−.07, p=.772), and Prosthodontist 1 (ρ=−.08, p=.741) Spearman correlation (N=20) RMSE vs Bur Prosthodontist 5 (ρ= .51, p = .007), Prosthodontist 2 (ρ=.46, p= .040), Prosthodontist 4 (ρ=−.07, p=.758), Prosthodontist 3 (ρ=.18, p=.445), and Prosthodontist 1 (ρ=.43, p=.059)

Conclusion: CNC Prototype achieved superior results in clinical simulation, attained on a modest budget with a modest level of research support. Work should continue on the next iteration of a prototype to address some of the limitations of movement, feedback, and emotional acceptance of a machine performing treatment from the perspective of a patient.