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

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

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.

No description available.

Continuum robots are the biologically inspired robots that mimic the behaviors of mammalian tongues, elephant trunks, and octopus arms. These robots feature a backboneless structure similar to their biological counterparts, such as termed muscular hydrostats. The drawbacks of two existing designs are examined and a new mechanical design that uses a single latex rubber tube as the central member is proposed, providing a design that is both simple and robust. Next, a novel verification procedure is applied to examine the validity of the proposed model in two different domains of applicability. A two-level electrical control scheme enables rapid prototyping and can be used to control the continuum robot remotely with a joystick via a Local Area Network (LAN). Next, a new geometrical approach to solve inverse kinematics for continuum type robot manipulators is introduced. Given the tip of a three-section robot, end-points of section 1 and section 2 are computed, and a complete inverse kinematics solution for a multisection continuum robot is then achieved by applying inverse kinematics to each section continuum trunk. Moreover, the algorithm provides a solution space rather than a single valid solution. Finally, the techniques involved in visualization of AirOctor/OctArm in 3D space in real-time are discussed.The algorithm has been tested with several system topologies.

published_or_final_version / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy

Insects posses an incredible ability to navigate their environment at high speed, despite having small brains and limited visual acuity. Through selective pressure they have evolved computationally efficient means for simultaneously performing navigation tasks and instantaneous control responses. The insect’s main source of information is visual, and through a hierarchy of processes this information is used for perception; at the lowest level are local neurons for detecting image motion and edges, at the higher level are interneurons to spatially integrate the output of previous stages. These higher level processes could be considered as models of the insect's environment, reducing the amount of information to only that which evolution has determined relevant. The scope of this thesis is experimenting with biologically inspired visual control of flying robots through information processing, models of the environment, and flight behaviour. In order to test these ideas I developed a custom quadrotor robot and experimental platform; the 'wasp' system. All algorithms ran on the robot, in real-time or better, and hypotheses were always verified with flight experiments. I developed a new optical flow algorithm that is computationally efficient, and able to be applied in a regular pattern to the image. This technique is used later in my work when considering patterns in the image motion field. Using optical flow in the log-polar coordinate system I developed attitude estimation and time-to-contact algorithms. I find that the log-polar domain is useful for analysing global image motion; and in many ways equivalent to the retinotopic arrange- ment of neurons in the optic lobe of insects, used for the same task. I investigated the role of depth in insect flight using two experiments. In the first experiment, to study how concurrent visual control processes might be combined, I developed a control system using the combined output of two algorithms. The first algorithm was a wide-field optical flow balance strategy and the second an obstacle avoidance strategy which used inertial information to estimate the depth to objects in the environment - objects whose depth was significantly different to their surround- ings. In the second experiment I created an altitude control system which used a model of the environment in the Hough space, and a biologically inspired sampling strategy, to efficiently detect the ground. Both control systems were used to control the flight of a quadrotor in an indoor environment. The methods that insects use to perceive edges and control their flight in response had not been applied to artificial systems before. I developed a quadrotor control system that used the distribution of edges in the environment to regulate the robot height and avoid obstacles. I also developed a model that predicted the distribution of edges in a static scene, and using this prediction was able to estimate the quadrotor altitude.

Many domains, such as geographical and biological sciences, can benefit from the ability of wireless sensor networks to provide long term, high temporal and spatial resolution sensing. Such networks must be able to trade off various requirements against each other to extend network lifetime while still providing useful, good quality data. The challenges faced by equipment in the field can very unpredictable and therefore a wireless sensor network should be able to cope with these challenges and return to a balanced state. Using readily available, low-cost components, this work was inspired by the human endocrine systems ability to maintain homeostasis, or balance, in a large number of parameters simultaneously. This work developed a number of endocrine inspired methods. These were aimed both at improving the power usage of nodes in a wireless sensor network and improving the quality of the data collected. Methods for improving power consumption and data quality were achieved. These methods were successfully deployed, for the purposes of environmental monitoring on a mesh network consisting of 20 nodes, for a period of almost 6 months. Analysis showed that the use of power by individual nodes was improved and that the endocrine inspired methods, aimed at improving data quality, were successful. Node lifetimes were extended, duplicate data reduced and the quality of data improved. The use of low-cost, readily available components was largely successful, and challenges and changes to these components were discussed.