Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sub-Linear Storage Cost

Robotic and animal mapping systems share many challenges and characteristics: they must function in a wide variety of environmental conditions, enable the robot or animal to navigate effectively to find food or shelter, and be computationally tractable from both a speed and storage perspective. With regards to map storage, the mammalian brain appears to take a diametrically opposed approach to all current robotic mapping systems. Where robotic mapping systems attempt to solve the data association problem to minimise representational aliasing, neurons in the brain intentionally break data association by encoding large (potentially unlimited) numbers of places with a single neuron. In this paper, we propose a novel method based on supervised learning techniques that seeks out regularly repeating visual patterns in the environment with mutually complementary co-prime frequencies, and an encoding scheme that enables storage requirements to grow sub-linearly with the size of the environment being mapped. To improve robustness in challenging real-world environments while maintaining storage growth sub-linearity, we incorporate both multi-exemplar learning and data augmentation techniques. Using large benchmark robotic mapping datasets, we demonstrate the combined system achieving high-performance place recognition with sub-linear storage requirements, and characterize the performance-storage growth trade-off curve. The work serves as the first robotic mapping system with sub-linear storage scaling properties, as well as the first large-scale demonstration in real-world environments of one of the proposed memory benefits of these neurons.Comment: Pre-print of article that will appear in the IEEE Robotics and Automation Letter

Scalable Tactile Sensing E-Skins Through Spatial Frequency Encoding

Most state-of-the-art tactile sensing arrays are not scalable to large numbers of sensing units due to their raster-scanned readout. This readout scheme results in a high degree of wiring complexity and a tradeoff between spatial and temporal resolution. In this thesis I present the use of spatial frequency encoding to develop asynchronous tactile sensor arrays with single-wire sensor transduction, no per-taxel electronics, and no scanning latency. I demonstrate this through two prototype devices, Neuroskin 1, which is developed using fabric-based e-textile materials, and Neuroskin 2, which is developed using fPCB. Like human skin, Neuroskin has a temporal resolution of 1 kHz and innate data compression where tactile data from an MxN Neuroskin is compressed into M+N values. Neuroskin 2 requires only four interface wires (regardless of its number of sensors) and can be easily scaled up through its development as an fPCB. To demonstrate the utility of the prototypes, Neuroskin was mounted onto a biomimetic robotic finger to palpate different textures and perform a texture discrimination task. Neuroskin 1 and 2 achieved 87% and 76% classification accuracy respectively in the texture discrimination task. Overall, the method of spatial-frequency encoding is theoretically scalable to support sensor arrays with thousands of sensing elements without latency, and the resolution of a Neuroskin array is only limited by the ADC sampling rate. Future tactile sensing systems can utilize the spatial frequency encoding architecture presented here to be dense, numerous, and flexible while retaining excellent temporal resolution

HRS: Rover Technologies

No abstract availabl

Design, evaluation, and control of nexus: a multiscale additive manufacturing platform with integrated 3D printing and robotic assembly.

Additive manufacturing (AM) technology is an emerging approach to creating three-dimensional (3D) objects and has seen numerous applications in medical implants, transportation, aerospace, energy, consumer products, etc. Compared with manufacturing by forming and machining, additive manufacturing techniques provide more rapid, economical, efficient, reliable, and complex manufacturing processes. However, additive manufacturing also has limitations on print strength and dimensional tolerance, while traditional additive manufacturing hardware platforms for 3D printing have limited flexibility. In particular, part geometry and materials are limited to most 3D printing hardware. In addition, for multiscale and complex products, samples must be printed, fabricated, and transferred among different additive manufacturing platforms in different locations, which leads to high cost, long process time, and low yield of products. This thesis investigates methods to design, evaluate, and control the NeXus, which is a novel custom robotic platform for multiscale additive manufacturing with integrated 3D printing and robotic assembly. NeXus can be used to prototype miniature devices and systems, such as wearable MEMS sensor fabrics, microrobots for wafer-scale microfactories, tactile robot skins, next generation energy storage (solar cells), nanostructure plasmonic devices, and biosensors. The NeXus has the flexibility to fixture, position, transport, and assemble components across a wide spectrum of length scales (Macro-Meso-Micro-Nano, 1m to 100nm) and provides unparalleled additive process capabilities such as 3D printing through both aerosol jetting and ultrasonic bonding and forming, thin-film photonic sintering, fiber loom weaving, and in-situ Micro-Electro-Mechanical System (MEMS) packaging and interconnect formation. The NeXus system has a footprint of around 4m x 3.5m x 2.4m (X-Y-Z) and includes two industrial robotic arms, precision positioners, multiple manipulation tools, and additive manufacturing processes and packaging capabilities. The design of the NeXus platform adopted the Lean Robotic Micromanufacturing (LRM) design principles and simulation tools to mitigate development risks. The NeXus has more than 50 degrees of freedom (DOF) from different instruments, precise evaluation of the custom robots and positioners is indispensable before employing them in complex and multiscale applications. The integration and control of multi-functional instruments is also a challenge in the NeXus system due to different communication protocols and compatibility. Thus, the NeXus system is controlled by National Instruments (NI) LabVIEW real-time operating system (RTOS) with NI PXI controller and a LabVIEW State Machine User Interface (SMUI) and was programmed considering the synchronization of various instruments and sequencing of additive manufacturing processes for different tasks. The operation sequences of each robot along with relevant tools must be organized in safe mode to avoid crashes and damage to tools during robots’ motions. This thesis also describes two demonstrators that are realized by the NeXus system in detail: skin tactile sensor arrays and electronic textiles. The fabrication process of the skin tactile sensor uses the automated manufacturing line in the NeXus with pattern design, precise calibration, synchronization of an Aerosol Jet printer, and a custom positioner. The fabrication process for electronic textiles is a combination of MEMS fabrication techniques in the cleanroom and the collaboration of multiple NeXus robots including two industrial robotic arms and a custom high-precision positioner for the deterministic alignment process

A force and thermal sensing skin for robots in human environments

Working together, heated and unheated temperature sensors can recognize contact with different materials and contact with the human body. As such, distributing these sensors across a robot’s body could be beneficial for operation in human environments. We present a stretchable fabric-based skin with force and thermal sensors that is suitable for covering areas of a robot’s body, including curved surfaces. It also adds a layer of compliance that conforms to manipulated objects, improving thermal sensing. Our design addresses thermal sensing challenges, such as the time to heat the sensors, the efficiency of sensing, and the distribution of sensors across the skin. It incorporates small self-heated temperature sensors on the surface of the skin that directly make contact with objects, improving the sensors’ response times. Our approach seeks to fully cover the robot’s body with large force sensing taxels, but treats temperature sensors as small, point-like sensors sparsely distributed across the skin. We present a mathematical model to help predict how many of these point-like temperature sensors should be used in order to increase the likelihood of them making contact with an object. To evaluate our design, we conducted tests in which a robot arm used a cylindrical end effector covered with skin to slide objects and press on objects made from four different materials. After assessing the safety of our design, we also had the robot make contact with the forearms and clothed shoulders of 10 human participants. With 2.0 s of contact, the actively-heated temperature sensors enabled binary classification accuracy over 90% for the majority of material pairs. The system could more rapidly distinguish between materials with large differences in their thermal effusivities (e.g., 90% accuracy for pine wood vs. aluminum with 0.5 s of contact). For discrimination between humans vs. the four materials, the skin’s force and thermal sensing modalities achieved 93% classification accuracy with 0.5 s of contact. Overall, our results suggest that our skin design could enable robots to recognize contact with distinct task-relevant materials and humans while performing manipulation tasks in human environments.M.S

Soft Biomimetic Finger with Tactile Sensing and Sensory Feedback Capabilities

The compliant nature of soft fingers allows for safe and dexterous manipulation of objects by humans in an unstructured environment. A soft prosthetic finger design with tactile sensing capabilities for texture discrimination and subsequent sensory stimulation has the potential to create a more natural experience for an amputee. In this work, a pneumatically actuated soft biomimetic finger is integrated with a textile neuromorphic tactile sensor array for a texture discrimination task. The tactile sensor outputs were converted into neuromorphic spike trains, which emulate the firing pattern of biological mechanoreceptors. Spike-based features from each taxel compressed the information and were then used as inputs for the support vector machine (SVM) classifier to differentiate the textures. Our soft biomimetic finger with neuromorphic encoding was able to achieve an average overall classification accuracy of 99.57% over sixteen independent parameters when tested on thirteen standardized textured surfaces. The sixteen parameters were the combination of four angles of flexion of the soft finger and four speeds of palpation. To aid in the perception of more natural objects and their manipulation, subjects were provided with transcutaneous electrical nerve stimulation (TENS) to convey a subset of four textures with varied textural information. Three able-bodied subjects successfully distinguished two or three textures with the applied stimuli. This work paves the way for a more human-like prosthesis through a soft biomimetic finger with texture discrimination capabilities using neuromorphic techniques that provides sensory feedback; furthermore, texture feedback has the potential to enhance the user experience when interacting with their surroundings. Additionally, this work showed that an inexpensive, soft biomimetic finger combined with a flexible tactile sensor array can potentially help users perceive their environment better