MICROCANTILEVER-BASED FORCE SENSING, CONTROL AND IMAGING

This dissertation presents a distributed-parameters base modeling framework for microcantilever (MC)-based force sensing and control with applications to nanomanipulation and imaging. Due to the widespread applications of MCs in nanoscale force sensing or atomic force microscopy with nano-Newton to pico-Newton force measurement requirements, precise modeling of the involved MCs is essential. Along this line, a distributed-parameters modeling framework is proposed which is followed by a modified robust controller with perturbation estimation to target the problem of delay in nanoscale imaging and manipulation. It is shown that the proposed nonlinear model-based controller can stabilize such nanomanipulation process in a very short time compared to available conventional methods. Such modeling and control development could pave the pathway towards MC-based manipulation and positioning. The first application of the MC-based (a piezoresistive MC) force sensors in this dissertation includes MC-based mass sensing with applications to biological species detection. MC-based sensing has recently attracted extensive interest in many chemical and biological applications due to its sensitivity, extreme applicability and low cost. By measuring the stiffness of MCs experimentally, the effect of adsorption of target molecules can be quantified. To measure MC\u27s stiffness, an in-house nanoscale force sensing setup is designed and fabricated which utilizes a piezoresistive MC to measure the force acting on the MC\u27s tip with nano-Newton resolution. In the second application, the proposed MC-based force sensor is utilized to achieve a fast-scan laser-free Atomic Force Microscopy (AFM). Tracking control of piezoelectric actuators in various applications including scanning probe microscopes is limited by sudden step discontinuities within time-varying continuous trajectories. For this, a switching control strategy is proposed for effective tracking of such discontinuous trajectories. A new spiral path planning is also proposed here which improves scanning rate of the AFM. Implementation of the proposed modeling and controller in a laser-free AFM setup yields high quality image of surfaces with stepped topographies at frequencies up to 30 Hz. As the last application of the MC-based force sensors, a nanomanipulator named here MM3A® is utilized for nanomanipulation purposes. The area of control and manipulation at the nanoscale has recently received widespread attention in different technologies such as fabricating electronic chipsets, testing and assembly of MEMS and NEMS, micro-injection and manipulation of chromosomes and genes. To overcome the lack of position sensor on this particular manipulator, a fused vision force feedback robust controller is proposed. The effects of utilization of the image and force feedbacks are individually discussed and analyzed for use in the developed fused vision force feedback control framework in order to achieve ultra precise positioning and optimal performance

An Abdominal Phantom with Tunable Stiffness Nodules and Force Sensing Capability for Palpation Training

Robotic phantoms enable advanced physical examination training before using human patients. In this paper, we present an abdominal phantom for palpation training with controllable stiffness liver nodules that can also sense palpation forces. The coupled sensing and actuation approach is achieved by pneumatic control of positive-granular jammed nodules for tunable stiffness. Soft sensing is done using the variation of internal pressure of the nodules under external forces. This paper makes original contributions to extend the linear region of the neo-Hookean characteristic of the mechanical behavior of the nodules by 140% compared to no-jamming conditions and to propose a method using the organ level controllable nodules as sensors to estimate palpation position and force with a root-means-quare error (RMSE) of 4% and 6.5%, respectively. Compared to conventional soft sensors, the method allows the phantom to sense with no interference to the simulated physiological conditions when providing quantified feedback to trainees, and to enable training following current bare-hand examination protocols without the need to wear data gloves to collect data.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) MOTION grant EP/N03211X/2 and EP/N03208X/1, and EPSRC RoboPatient grant EP/T00603X/

A Biologically Inspired Controllable Stiffness Multimodal Whisker Follicle

This thesis takes a soft robotics approach to understand the computational role of a soft whisker follicle with mechanisms to control the stiffness of the whisker. In particular, the thesis explores the role of the controllable stiffness whisker follicle to selectively favour low frequency geometric features of an object or the high frequency texture features of the object.Tactile sensing is one of the most essential and complex sensory systems for most living beings. To acquire tactile information and explore the environment, animals use various biological mechanisms and transducing techniques. Whiskers, or vibrissae are a form of mammalian hair, found on almost all mammals other than homo sapiens. For many mammals, and especially rodents, these whiskers are essential as a means of tactile sensing.The mammalian whisker follicle contains multiple sensory receptors strategically organised to capture tactile sensory stimuli of different frequencies via the vibrissal system. Nocturnal mammals such as rats heavily depend on whisker based tactile perception to find their way through burrows and identify objects. There is diversity in the whiskers in terms of the physical structure and nervous innervation. The robotics community has developed many different whisker sensors inspired by this biological basis. They take diverse mechanical, electronic, and computational approaches to use whiskers to identify the geometry, mechanical properties, and objects' texture. Some work addresses specific object identification features and others address multiple features such as texture and shape etc. Therefore, it is vital to have a comprehensive discussion of the literature and to understand the merits of bio-inspired and pure-engineered approaches to whisker-based tactile perception.The most important contribution is the design and use of a novel soft whisker follicle comprising two different frequency-dependent data capturing modules to derive more profound insights into the biological basis of tactile perception in the mammalian whisker follicle. The new insights into the biological basis of tactile perception using whiskers provide new design guidelines to develop efficient robotic whiskers

Scalability study for robotic hand platform

The goal of this thesis project was to determine the lower limit of scale for the RIT robotic grasping hand. This was accomplished using a combination of computer simulation and experimental studies. A force analysis was conducted to determine the size of air muscles required to achieve appropriate contact forces at a smaller scale. Input variables, such as the actuation force and tendon return force, were determined experimentally. A dynamic computer model of the hand system was then created using Recurdyn. This was used to predict the contact (grasping) force of the fingers at full-scale, half-scale, and quarter-scale. Correlation between the computer model and physical testing was achieved for both a life-size and half-scale finger assembly. To further demonstrate the scalability of the hand design, both half and quarter-scale robotic hand rapid prototype assemblies were built using 3D printing techniques. This thesis work identified the point where further miniaturization would require a change in the manufacturing process to micro-fabrication. Several techniques were compared as potential methods for making a production intent quarter-scale robotic hand. Investment casting, Swiss machining, and Selective Laser Sintering were the manufacturing techniques considered. A quarter-scale robotic hand tested the limits of each technology. Below this scale, micro-machining would be required. The break point for the current actuation method, air muscles, was also explored. Below the quarter-scale, an alternative actuation method would also be required. Electroactive Polymers were discussed as an option for the micro-scale. In summary, a dynamic model of the RIT robotic grasping hand was created and validated as scalable at full and half-scales. The model was then used to predict finger contact forces at the quarter-scale. The quarter-scale was identified as the break point in terms of the current RIT robotic grasping hand based on both manufacturing and actuation. A novel, prototype quarter-scale robotic hand assembly was successfully built by an additive manufacturing process, a high resolution 3D printer. However, further miniaturization would require alternate manufacturing techniques and actuation mechanisms

Control oriented modelling of an integrated attitude and vibration suppression architecture for large space structures

This thesis is divided into two parts. The main focus of the research, namely active vibration control for large flexible spacecraft, is exposed in Part I and, in parallel, the topic of machine learning techniques for modern space applications is described in Part II. In particular, this thesis aims at proposing an end-to-end general architecture for an integrated attitude-vibration control system, starting from the design of structural models to the synthesis of the control laws. To this purpose, large space structures based on realistic missions are investigated as study cases, in accordance with the tendency of increasing the size of the scientific instruments to improve their sensitivity, being the drawback an increase of its overall flexibility. An active control method is therefore investigated to guarantee satisfactory pointing and maximum deformation by avoiding classical stiffening methods. Therefore, the instrument is designed to be supported by an active deployable frame hosting an optimal minimum set of collocated smart actuators and sensors. Different spatial configurations for the placement of the distributed network of active devices are investigated, both at closed-loop and open-loop levels. Concerning closed-loop techniques, a method to optimally place the poles of the system via a Direct Velocity Feedback (DVF) controller is proposed to identify simultaneously the location and number of active devices for vibration control with an in-cascade optimization technique. Then, two general and computationally efficient open-loop placement techniques, namely Gramian and Modal Strain Energy (MSE)-based methods, are adopted as opposed to heuristic algorithms, which imply high computational costs and are generally not suitable for high-dimensional systems, to propose a placement architecture for generically shaped tridimensional space structures. Then, an integrated robust control architecture for the spacecraft is presented as composed of both an attitude control scheme and a vibration control system. To conclude the study, attitude manoeuvres are performed to excite main flexible modes and prove the efficacy of both attitude and vibration control architectures. Moreover, Part II is dedicated to address the problem of improving autonomy and self-awareness of modern spacecraft, by using machine-learning based techniques to carry out Failure Identification for large space structures and improving the pointing performance of spacecraft (both flexible satellite with sloshing models and small rigid platforms) when performing repetitive Earth Observation manoeuvres

Development of multifunctional nano-probes for neuroscience research

The contribution of nanotechnology to the field of Neuroscience is increasing exponentially. In order to understand the relationship of structure to function at the cellular level, and to decipher the mysteries of nervous system, development of new tools to manipulate and measure cellular function at a local level is necessary. It is a continuing challenge to develop easily fabricated, multipurpose nano-probes which are able to target neural nanostructures for the local manipulation and measurement of functional responses. This thesis is focused on the fabrication, characterisation and implementation of a nano-pipette on a Scanning Ion Conductance Microscopy (SICM). The nano-pipette mounted on a SICM set-up acts as a proximity sensor for non-contact imaging of cellular features. SICM platform to accommodate electrochemical experiments is discussed. In particular, the development of a novel electrochemical probe, fabricated by pyrolytic decomposition of carbon within a quartz nano-pipette is discussed. This method is simple and carbon nano-electrodes of variable size can be fabricated in a single step. The nano-pipette‘s distance controlled feedback system was exploited for local delivery of chemicals to neuronal structures. Experimental and theoretical data are compared in order to calculate the concentration of molecules at the tip of the nano-pipette as a function of the driving force (voltage or pressure) and distance. The quantitative delivery of molecules from a 100 nm nano-pipette is demonstrated. In particular capsaicin-filled nano-pipette is used to trigger capsaicin-sensitive TRPV1 receptors in sensory neurons and transfected cells. Finally some preliminary results for the future development and potential application of nano-pipettes are shown. The nano-pipette is easily fabricated and is shown to be multi-functional. It provides an invaluable tool in the investigation of the nano-physiology of neurons. The SICM multipoint delivery competence can contribute to the various endeavours in drug discovery and to the yield of in vitro pharmacological assays.Open Acces

Two-photon all-optical interrogation of mouse barrel cortex during sensory discrimination

The neocortex supports a rich repertoire of cognitive and behavioural functions, yet the rules, or neural ‘codes’, that determine how patterns of cortical activity drive perceptual processes remain enigmatic. Experimental neuroscientists study these codes through measuring and manipulating neuronal activity in awake behaving subjects, which allows links to be identified between patterns of neural activity and ongoing behaviour functions. In this thesis, I detail the application of novel optical techniques for simultaneously recording and manipulating neurons with cellular resolution to examine how tactile signals are processed in sparse neuronal ensembles in mouse somatosensory ‘barrel’ cortex. To do this, I designed a whisker-based perceptual decision-making task for head-fixed mice, that allows precise control over sensory input and interpretable readout of perceptual choice. Through several complementary experimental approaches, I show that task performance is exquisitely coupled to barrel cortical activity. Using two- photon calcium imaging to simultaneously record from populations of barrel cortex neurons, I demonstrate that different subpopulations of neurons in layer 2/3 (L2/3) show selectivity for contralateral and ipsilateral whisker input during behaviour. To directly test whether these stimulus-tuned groups of neurons differentially impact perceptual decision-making I performed patterned photostimulation experiments to selectively activate these functionally defined sets of neurons and assessed the resulting impact on behaviour and the local cortical network in layer 2/3. In contrast with the expected results, stimulation of sensory-coding neurons appeared to have little perceptual impact on task performance. However, activation of non- stimulus coding neurons did drive decision biases. These results challenge the conventional view that strongly sensory responsive neurons carry more perceptual weight than non-responsive sensory neurons during perceptual decision-making. Furthermore, patterned photostimulation revealed and imposed potent surround suppression in L2/3, which points to strong lateral inhibition playing a dominant role in shaping spatiotemporally sparse activity patterns. These results showcase the utility of combined patterned photostimulation methods and population calcium imaging for revealing and testing neural circuit function during sensorimotor behaviour and provide new perspectives on sensory coding in barrel cortex

Autonomous robotic nanofabrication with reinforcement learning

The ability to handle single molecules as effectively as macroscopic building-blocks would enable the construction of complex supramolecular structures inaccessible to self-assembly. The fundamental challenges obstructing this goal are the uncontrolled variability and poor observability of atomic-scale conformations. Here, we present a strategy to work around both obstacles, and demonstrate autonomous robotic nanofabrication by manipulating single molecules. Our approach employs reinforcement learning (RL), which finds solution strategies even in the face of large uncertainty and sparse feedback. We demonstrate the potential of our RL approach by removing molecules autonomously with a scanning probe microscope from a supramolecular structure -- an exemplary task of subtractive manufacturing at the nanoscale. Our RL agent reaches an excellent performance, enabling us to automate a task which previously had to be performed by a human. We anticipate that our work opens the way towards autonomous agents for the robotic construction of functional supramolecular structures with speed, precision and perseverance beyond our current capabilities.Comment: 3 figure