The remarkable attachment system of geckos has inspired the development of dry microfiber adhesives through the last two decades. Some of the notable characteristics of gecko-inspired fibrillar adhesives include: strong, directional, and controllable adhesion to smooth and rough surfaces in air, vacuum, and under water; ability to maintain strong adhesion during repeated use; anti-fouling and self-cleaning after contamination. Given these outstanding qualities, fibrillar adhesives promise an extensive range of use in industrial, robotic, manufacturing, medical, and consumer products. Significant advancements have been made in the design of geckoinspired microfiber adhesives with the characteristic properties listed above, with the exception of the anti-fouling and self-cleaning features. The self-cleaning mechanism of the gecko’s adhesion system plays an important role to its ability to remain sticky in various environments. Similarly, enabling self-cleaning capability for synthetic microfiber adhesives will lead to robust performance in various areas of application. Presently, the practical use of fibrillar adhesives is restricted mainly to clean environments, where they are free from contaminants. The goal of this thesis is to conduct a detailed study of the mechanisms and mechanics of contact-based self-cleaning of gecko-inspired microfiber adhesives. This work focuses on contact self-cleaning mechanisms, as a more practical approach to cleaning. Previous studies on the cleaning of microfiber adhesives have mostly focused on mechanisms that involve complete removal of the contaminants from the adhesive. In this thesis, a second cleaning process is proposed whereby particles are removed from the tip of the microfibers and embedded between adjacent microfibers or in grooves patterned onto the adhesive, where they are no longer detrimental to the performance of the adhesive. In this work, a model of adhesion for microfiber adhesives that take the deformation of the backing layer under individual microfiber is developed. The dependence of adhesion of microfiber adhesives on the rate of unloading is also modeled and verified using experiments. The models of adhesion presented are later used to study the mechanics of contact self-cleaning of microfiber adhesives. Three major categories of self-cleaning are identified as wet self-cleaning, dynamic self-cleaning, and contact self-cleaning. A total of seven self-cleaning mechanisms that are associated with these categories are also presented and discussed. Results from the self-cleaning model and experiments show that shear loading plays an important role in self-cleaning. The underlying mechanism of contact self-cleaning due to normal and shear loading for spherical contaminants is found to be the particle rolling between the adhesive and a contacted substrate. Results from the model and experiments also show that small microfiber tips (much less than the size of the contaminants) are favorable for self-cleaning. On the other hand, large microfiber tips (much larger than the size of the contaminants) are favorable for anti-fouling of the microfiber adhesive. Results from this work suggests that the sub-micrometer size of the gecko’s adhesive fibers and the lamellae under the gecko toes contribute to its outstanding self-cleaning performance. The results presented in this thesis can be implemented in the design of microfiber adhesives with robust adhesion, self-cleaning and anti-fouling characteristic, for use in numerous applications and in various environments.

Thesis (Ph. D.)--State University of New York at Binghamton, Department of Mathematical Sciences, 2008. / Includes bibliographical references.

The thesis focused on the development and analysis of a distributed parameter model that apply to a class of an autonomous hyper-redundant slender robotic systems interacting with the environment. The class of robotic devices that will be implemented based on the modelling in this thesis, is intended to be autonomously deployed in unknown, unstructured environments, in which it has to accomplish different missions by being able to robustly negotiate unknown obstacles and unpredictable and unmodelled irregularities. Therefore the mechanical models presented here are inspired by some features of a class of organisms - millipedes and centipedes - that possess many of these capabilities. Specifically, these organisms posses flexible slender bodies whose shape morphs according to the curvature of the terrain on which they operate, and possess a highly redundant system of legs that couple the body with the terrain providing propulsion for forward or backward motion, with the high number of legs ensuring a robust distributed contact even on very irregular substrates. The mechanical model that naturally captures the structure of millipede bodies is the Timoshenko beam, which is therefore adopted here. Moreover, the coupling with the environment is modeled by a system of compliant elements, that provides a distributed support analogous to the one exerted by millipedes' legs; such support provides a distributed force that in a control framework is treated as the actuation for shape morphing, so that the body of the system deforms according to the curvature of the substrate. By using a Lagrangian mechanics approach, the evolution of the system is described in a suitable product Hilbert space, in which rigid body degrees of freedom and deformations are coupled. This formulation allows to pose a distributed parameter control problem in which shape morphing and locomotion are dictated by the interaction with the substrate, which in this case is approximated as rigid (that is, the profile of the substrate is not affected by the interaction with the system). Additionally, by modeling the material response of the substrate with a simple linear viscoelastic model, we pose an estimation problem in which, by measuring deformations and/or stresses on the body represented by the beam, we can infer the material properties of the substrate. In this case, the overall coupled system is modelled as a beam on a multi-layer viscoelastic foundation. Predictions of this sensor model are in good agreement with published results, suggesting that the system can be used in a versatile way as an autonomous agent operating in a generic environment, and simultaneously as a sensor that could inform the action of the system itself, or that could be used to monitor the environment. The modeling work done in this study opens the possibility for the implementation in engineering systems applied to environmental monitoring and health applications, in which we envision the system to be used to estimate material properties of living tissues, that can be correlated to the diagnosis of classes of diseases.

The objective of this research is to discover principles of ambulatory locomotion on homogeneous and heterogeneous granular substrates and create models of animal and robot interaction within such environments. Since interaction with natural substrates is too complicated to model, we take a robophysics approach – we create a terrain generation system where properties of heterogeneous multi-component substrates can be systematically varied to emulate a wide range of natural terrain properties such as compaction, orientation, obstacle shape/size/distribution, and obstacle mobility within the substrate. A schematic of the proposed system is discussed in detail in the body of this dissertation. Control of such substrates will allow for the systematic exploration of parameters of substrate properties, particularly substrate stiffness and heterogeneities. With this terrain creation system, we systematically explore locomotor strategies of simplified laboratory robots when traversing over different terrain properties. A key feature of this proposed work is the ability to generate general interaction models of locomotor appendages with such complex substrates. These models will aid in the design and control of future robots with morphologies and control strategies that allow for effective navigation on a large diversity of terrains, expanding the scope of terramechanics from large tracked and treaded vehicles on homogeneous ground to arbitrarily shaped and actuated locomotors moving on complex heterogeneous terrestrial substrates.

This project applies principles from the field of Computational Neuroscience to Robotics research, in particular to develop systems inspired by how nature manages to solve sensorimotor coordination tasks. The overall aim has been to build a self-organising sensorimotor system using biologically inspired techniques based upon human cortical development which can in the future be implemented in neuromorphic hardware. This can then deliver the benefits of low power consumption and real time operation but with flexible learning onboard autonomous robots. A core principle is the Self-Organising Feature Map which is based upon the theory of how 2D maps develop in real cortex to represent complex information from the environment. A framework for developing feature maps for both motor and visual directional selectivity representing eight different directions of motion is described as well as how they can be coupled together to make a basic visuomotor system. In contrast to many previous works which use artificially generated visual inputs (for example, image sequences of oriented moving bars or mathematically generated Gaussian bars) a novel feature of the current work is that the visual input is generated by a DVS 128 silicon retina camera which is a neuromorphic device and produces spike events in a frame-free way. One of the main contributions of this work has been to develop a method of autonomous regulation of the map development process which adapts the learning dependent upon input activity. The main results show that distinct directionally selective maps for both the motor and visual modalities are produced under a range of experimental scenarios. The adaptive learning process successfully controls the rate of learning in both motor and visual map development and is used to indicate when sufficient patterns have been presented, thus avoiding the need to define in advance the quantity and range of training data. The coupling training experiments show that the visual input learns to modulate the original motor map response, creating a new visual-motor topological map.

Humans utilize the inherent biomechanical compliance present in their fingers for increased stability and dexterity during manipulation tasks. While series elastic actuation has been explored, little research has been performed on the role of joint compliance arranged in parallel with the actuators. The goal of this thesis is to demonstrate, through simulation studies and experimental analyses, the advantages gained by employing human-like passive compliance in finger joints when grasping. We first model two planar systems: a single 2-DOF (degree of freedom) finger and a pair of 2-DOF fingers grasping an object. In each case, combinations of passive joint compliance and active stiffness control are implemented, and the impulse disturbance responses are compared. The control is carried out at a limited sampling frequency, and an energy analysis is performed to investigate stability. Our approach reveals that limited controller frequency leads to increased actuator energy input and hence a less stable system, and human-like passive parallel compliance can improve stability and robustness during grasping tasks. Then, an experimental setup is designed consisting of dual 2-DOF tendon-driven fingers. An impedance control law for two-fingered object manipulation is developed, using a novel friction compensation technique for improved actuator force control. This is used to experimentally quantify the advantages of parallel compliance during dexterous manipulation tasks, demonstrating smoother trajectory tracking and improved stability and robustness to impacts. / text

One of the greatest challenges in controlling robotic hands is grasping and manipulating objects in unstructured and uncertain environments. Robotic hands are typically too rigid to react against unexpected impacts and disturbances in order to prevent damage. The human hands have great versatility and robustness due, in part, to the passive compliance and damping. Designing mechanical elements that are inspired by the nonlinear joint compliance of human hands is a promising solution to achieve human-like grasping and manipulation. However, the exact role of biomechanical elements in realizing joint stiffness is unknown. We conducted a series of experiments to investigate nonlinear stiffness and damping of the metacarpophalangeal (MCP) joint at the index finger. We designed a custom-made mechanism to integrate electromyography sensors (EMGs) and a motion capture system to collect data from 19 subjects. We investigated the relative contributions of muscle-tendon units and the MCP capsule ligament complex to joint stiffness with subject-specific modeling. The results show that the muscle-tendon units provide limited contribution to the passive joint compliance. This findings indicate that the parallel compliance, in the form of the capsule-ligament complex, is significant in defining the passive properties of the hand. To identify the passive damping, we used the hysteresis loops to investigate the energy dissipation function. We used symbolic regression and principal component analysis to derive and interpret the damping models. The results show that the nonlinear viscous damping depends on the cyclic frequency, and fluid and structural types of damping also exist at the MCP joint. Inspired by the nonlinear stiffness of the MCP joint, we developed a miniaturized mechanism that uses pouring liquid plastic to design energy storing elements. The key innovations in this design are: a) a set of nonlinear elasticity of compliant materials, b) variable pulley configurations to tune the stiffness profile, and c) pretension mechanism to scale the stiffness profile. The design exhibits human-like passive compliance. By taking advantage of miniaturized joint size and additive manufacturing, we incorporated the novel joint design in a novel robotic manipulator with six series elastic actuators (SEA). The robotic manipulator has passive joint compliance with the intrinsic property of human hands. To validate the system, we investigated the Cartesian stiffness of grasping with low-level force control. The results show that that the overall system performs a great force tracking with position feedback. The parallel compliance decreases the motor efforts and can stabilize the system. / text

ITC/USA 2014 Conference Proceedings / The Fiftieth Annual International Telemetering Conference and Technical Exhibition / October 20-23, 2014 / Town and Country Resort & Convention Center, San Diego, CA / In this paper, we propose a novel technique using a single antenna for direction of arrival (DOA) estimation of broadband microwave signals. We designed and fabricated a microstrip-leaky-wave receiving antenna, which has good matching and reasonable radiation efficiency in the frequency range of interest: 2 - 3.5 GHz. Because the frequency response of the antenna is strongly incident-angle dependent, by using the spectral information at the antenna, we are able to estimate the DOA of a broadband microwave signal with a high degree of accuracy. Simulations and experiments show that the proposed technique enables good DOA estimation performance within a 90˚ range.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2008. / Includes bibliographical references (leaves 368-377). Also available in electronic version.

Cloud computing environments mainly focus on the delivery of resources, platforms, and infrastructure as services to users over the Internet. More specifically, Cloud promises user access to a scalable amount of resources, making use of the elasticity on the provisioning of recourses by scaling them up and down depending on the demand. The cloud technology has gained popularity in recent years as the next big step in the IT industry. The number of users of Cloud services has been increasing steadily, so the need for efficient task scheduling is crucial for improving and maintaining performance. Moreover, those users have different SLAs that imposes different demands on the cloud system. In this particular case, a scheduler is responsible for assigning tasks to virtual machines in an effective and efficient matter to meet with the QoS promised to users. The scheduler needs to adapt to changes in the cloud environment along with defined demand requirements. Hence, an Adjustable and Configurable bio-inspired scheduling heuristic for cloud based systems (ACBH) is suggested. We also present an extensively comparative performance study on bio-inspired scheduling algorithms namely Ant Colony Optimization (ACO) and Honey Bee Optimization (HBO). Furthermore, a networking scheduling algorithm is also evaluated, which comprises Random Biased Sampling (RBS). The study of bio-inspired techniques concluded that all the bio-inspired algorithms follow the same flow that was later used in the development of (ACBH). The experimental results have shown that ACBH has a 90% better execution time that it closest rival which is ACO. ACBH has a better performance in terms of the fairness between execution time differences between tasks. HBO shows better scheduling when the objective consists mainly of costs. However, when there is multiple optimization objectives ACBH performs the best due to its configurability and adaptability.