Novel Integrated System Architecture for an Autonomous Jumping Micro-Robot

As the capability and complexity of robotic platforms continue to evolve from the macro to micro-scale, innovation of such systems is driven by the notion that a robot must be able to sense, think, and act [1]. The traditional architecture of a robotic platform consists of a structural layer upon which, actuators, controls, power, and communication modules are integrated for optimal system performance. The structural layer, for many micro-scale platforms, has commonly been implemented using a silicon die, thus leading to robotic platforms referred to as "walking chips" [2]. In this thesis, the first-ever jumping microrobotic platform is demonstrated using a hybrid integration approach to assemble on-board sensing and power directly onto a polymer chassis. The microrobot detects a change in light intensity and ignites 0.21mg of integrated nanoporous energetic silicon, resulting in 246µJ of kinetic energy and a vertical jump height of 8cm

Force sensing enhancement of robot system

At present there is a general industrial need to improve robot performance. Force feedback, which involves sensing and actuation, is one means of improving the relative position between the workpiece and the end-effector. In this research work various causes of errors and poor robot performance are identified. Several methods of improving the performance of robotic systems are discussed. As a result of this research, a system was developed which is interposed between the wrist and the gripper of the manipulator. This system integrates a force sensor with a micro-manipulator, via an electronic control unit, with a micro-computer to enhance a robot system. The force sensor, the micromanipulator and the electronic control unit, were all designed and manufactured at the robotic centre of Middlesex Polytechnic. The force feedback is provided by means of strain gauges and the associated bridge circuitry. Control algorithms which define the relationship between the force detected and the motion required are implemented in the software. The software is capable of performing two specific tasks in real time, these are: 1- Inserting a peg into a hole 2- Following an unknown geometric path A rig was designed and manufactured to enable the robot to follow different geometric shapes and paths in which force control was achieved mainly by control of the micro-manipulator

ENABLING HARDWARE TECHNOLOGIES FOR AUTONOMY IN TINY ROBOTS: CONTROL, INTEGRATION, ACTUATION

The last two decades have seen many exciting examples of tiny robots from a few cm3 to less than one cm3. Although individually limited, a large group of these robots has the potential to work cooperatively and accomplish complex tasks. Two examples from nature that exhibit this type of cooperation are ant and bee colonies. They have the potential to assist in applications like search and rescue, military scouting, infrastructure and equipment monitoring, nano-manufacture, and possibly medicine. Most of these applications require the high level of autonomy that has been demonstrated by large robotic platforms, such as the iRobot and Honda ASIMO. However, when robot size shrinks down, current approaches to achieve the necessary functions are no longer valid. This work focused on challenges associated with the electronics and fabrication. We addressed three major technical hurdles inherent to current approaches: 1) difficulty of compact integration; 2) need for real-time and power-efficient computations; 3) unavailability of commercial tiny actuators and motion mechanisms. The aim of this work was to provide enabling hardware technologies to achieve autonomy in tiny robots. We proposed a decentralized application-specific integrated circuit (ASIC) where each component is responsible for its own operation and autonomy to the greatest extent possible. The ASIC consists of electronics modules for the fundamental functions required to fulfill the desired autonomy: actuation, control, power supply, and sensing. The actuators and mechanisms could potentially be post-fabricated on the ASIC directly. This design makes for a modular architecture. The following components were shown to work in physical implementations or simulations: 1) a tunable motion controller for ultralow frequency actuation; 2) a nonvolatile memory and programming circuit to achieve automatic and one-time programming; 3) a high-voltage circuit with the highest reported breakdown voltage in standard 0.5 μm CMOS; 4) thermal actuators fabricated using CMOS compatible process; 5) a low-power mixed-signal computational architecture for robotic dynamics simulator; 6) a frequency-boost technique to achieve low jitter in ring oscillators. These contributions will be generally enabling for other systems with strict size and power constraints such as wireless sensor nodes

2020 NASA Technology Taxonomy

This document is an update (new photos used) of the PDF version of the 2020 NASA Technology Taxonomy that will be available to download on the OCT Public Website. The updated 2020 NASA Technology Taxonomy, or "technology dictionary", uses a technology discipline based approach that realigns like-technologies independent of their application within the NASA mission portfolio. This tool is meant to serve as a common technology discipline-based communication tool across the agency and with its partners in other government agencies, academia, industry, and across the world

Faculty of Engineering and Design. Research Review

STUDENTS AND ACADEMICS - This publication introduces you to the department or school and then each faculty member’s research areas, research applications, and their most recent activities. A comprehensive index can be found at the back of this publication to help guide you by specific areas of interest, as well as point out interdisciplinary topics and researchers. INDUSTRY LEADERS - This publication includes information regarding specific facilities, labs, and research areas of departments and schools as well as individual faculty members and researchers. A comprehensive index can be found at the back of this publication to help guide you by specific areas of interest, as well as point out interdisciplinary topics and researchers

A low-cost, wireless, 3-D-printed custom armband for sEMG hand gesture recognition

Wearable technology can be employed to elevate the abilities of humans to perform demanding and complex tasks more efficiently. Armbands capable of surface electromyography (sEMG) are attractive and noninvasive devices from which human intent can be derived by leveraging machine learning. However, the sEMG acquisition systems currently available tend to be prohibitively costly for personal use or sacrifice wearability or signal quality to be more affordable. This work introduces the 3DC Armband designed by the Biomedical Microsystems Laboratory in Laval University; a wireless, 10-channel, 1000 sps, dry-electrode, low-cost ( 150 USD) myoelectric armband that also includes a 9-axis inertial measurement unit. The proposed system is compared with the Myo Armband by Thalmic Labs, one of the most popular sEMG acquisition systems. The comparison is made by employing a new offline dataset featuring 22 able-bodied participants performing eleven hand/wrist gestures while wearing the two armbands simultaneously. The 3DC Armband systematically and significantly (p < 0.05) outperforms the Myo Armband, with three different classifiers employing three different input modalities when using ten seconds or more of training data per gesture. This new dataset, alongside the source code, Altium project and 3-D models are made readily available for download within a Github repository

Building Blocks for Adaptive Modular Sensing Systems

This thesis contributes towards the development of systems and strategies by which sensor and actuator components can be combined to produce flexible and robust sensor systems for a given application. A set of intelligent modular blocks (building blocks) have been created from which composite sensors (made up of multiple sensor and actuator components) can be rapidly reconfigured for the construction of Adaptive Modular Sensing Systems. The composite systems are expected to prove useful in several application domains including industrial control, inspection systems, mobile robotics, monitoring and data acquisition. The intelligent building blocks, referred to as transducer interface modules, contain embedded knowledge about their capabilities and how they can interact with other modules. These modules encapsulate a general purpose modular hardware architecture that provides an interface between the sensors, the actuators, and the communication medium. The geometry of each transducer interface module is a cube. A connector mechanism implemented on each face of the module enables physical connection of the modules. Each module provides a core functionality and can be connected to other modules to form more capable composite sensors. Once the modules are combined, the capabilities (e.g., range, resolution, sample rate, etc.) and functionality (e.g., temperature measurement) of the composite sensor is determined and communicated to other sensors in the enviornment. For maximum flexibility, a distributed software architecture is executed on the blocks to enable automatic acquisition of configuration-specific algorithms. This logical algorithm imparts a collective identity to the composite group, and processes data based on the capabilities and functionalities of the transducers present in the system. A knowledge representation scheme allows each module in the composite group to store and communicate its functionality and capabilities to other connected modules in the system

MACHINE LEARNING AUGMENTATION MICRO-SENSORS FOR SMART DEVICE APPLICATIONS

Novel smart technologies such as wearable devices and unconventional robotics have been enabled by advancements in semiconductor technologies, which have miniaturized the sizes of transistors and sensors. These technologies promise great improvements to public health. However, current computational paradigms are ill-suited for use in novel smart technologies as they fail to meet their strict power and size requirements. In this dissertation, we present two bio-inspired colocalized sensing-and-computing schemes performed at the sensor level: continuous-time recurrent neural networks (CTRNNs) and reservoir computers (RCs). These schemes arise from the nonlinear dynamics of micro-electro-mechanical systems (MEMS), which facilitates computing, and the inherent ability of MEMS devices for sensing. Furthermore, this dissertation addresses the high-voltage requirements in electrostatically actuated MEMS devices using a passive amplification scheme. The CTRNN architecture is emulated using a network of bistable MEMS devices. This bistable behavior is shown in the pull-in, the snapthrough, and the feedback regimes, when excited around the electrical resonance frequency. In these regimes, MEMS devices exhibit key behaviors found in biological neuronal populations. When coupled, networks of MEMS are shown to be successful at classification and control tasks. Moreover, MEMS accelerometers are shown to be successful at acceleration waveform classification without the need for external processors. MEMS devices are additionally shown to perform computing by utilizing the RC architecture. Here, a delay-based RC scheme is studied, which uses one MEMS device to simulate the behavior of a large neural network through input modulation. We introduce a modulation scheme that enables colocalized sensing-and-computing by modulating the bias signal. The MEMS RC is tested to successfully perform pure computation and colocalized sensing-and-computing for both classification and regression tasks, even in noisy environments. Finally, we address the high-voltage requirements of electrostatically actuated MEMS devices by proposing a passive amplification scheme utilizing the mechanical and electrical resonances of MEMS devices simultaneously. Using this scheme, an order-of-magnitude of amplification is reported. Moreover, when only electrical resonance is used, we show that the MEMS device exhibits a computationally useful bistable response. Adviser: Dr. Fadi Alsalee