We report an adaptive capacitive transimpedance amplifier circuit for room temperature sensing of short wave infrared (SWIR) radiation. The photocircuit reduces junction leakage current by measuring the current across a photodiode held at zero bias. This is critical to enable room temperature SWIR detection using materials with smaller bandgaps (and higher leakage current) such as InGaAs, which are typically operated at cooled temperatures. The transimpedance amplifier incorporates a floating gate current mirror in order to precisely cancel offset using nonvolatile analog storage. We experimentally verify that we are able to precisely tune the input offset and demonstrate a reduction in dark current of an InGaAs photodiode by two orders of magnitude, from 700pA to 2.25pA, when comparing similar adaptive and non-adaptive circuits. These circuits have been fabricated in a standard 0.6μm CMOS process.

In this paper we present a simulation framework for testing and benchmarking the photon-limited performance of optic flow processing techniques. We explore the performance of “traditional” gradient-based and feature-based optic flow algorithms as well as the “biologically-inspired” elementary motion detector. We show that biologically-inspired spatial pooling techniques can successfully be implemented in the gradient-based image interpolation algorithm and the elementary motion detector to extend low-light capabilities by more than one order of magnitude. We also show that block matching algorithms can accurately compute optic flow even when a single frame may capture <;1000 photons. This framework provides a tool to further explore the mechanisms that underlie the observed low-light performance of biological vision systems and to understand the relative performance of different approaches for implementing optic flow in hardware.

This study describes the design and implementation of several bioinspired algorithms for providing guidance to an ultra-lightweight micro-aerial vehicle (MAV) using a 2.6 g omnidirectional vision sensor. Using this visual guidance system we demonstrate autonomous speed control, centring, and heading stabilisation on board a 30 g MAV flying in a corridor-like environment. In addition to the computation of wide-field optic flow, the comparatively high-resolution omnidirectional imagery provided by this sensor also offers the potential for image-based algorithms such as landmark recognition to be implemented in the future.

Achieving computer vision on microscale devices is a challenge. On these platforms, the power and mass constraints are severe enough for even the most common computations (matrix manipulations, convolution, etc.) to be difficult. This paper proposes and analyzes a class of miniature vision sensors that can help overcome these constraints. These sensors reduce power requirements through template-based optical convolution, and they enable a wide field-of-view within a small form through a refractive optical design. We describe the tradeoffs between the field-of-view, volume, and mass of these sensors and we provide analytic tools to navigate the design space. We demonstrate milliscale prototypes for computer vision tasks such as locating edges, tracking targets, and detecting faces. Finally, we utilize photolithographic fabrication tools to further miniaturize the optical designs and demonstrate fiducial detection onboard a small autonomous air vehicle.

We present experimental results on the controlled vertical flight of a flapping-wing flying microrobot, in which for the first time an on-board sensing system is used for measuring the microrobot's altitude for feedback control. Both the control strategy and the sensing system are biologically inspired. The control strategy relies on amplitude modulation mediated by optical flow. The research presented here is a key step toward achieving the goal of complete autonomy for flying microrobots, since this demonstrates that strategies for controlling flapping-wing microrobots in vertical flight can rely on optical flow sensors.

Achieving computer vision on micro-scale devices is a challenge. On these platforms, the power and mass constraints are severe enough for even the most common computations (matrix manipulations, convolution, etc.) to be difficult. This paper proposes and analyzes a class of miniature vision sensors that can help overcome these constraints. These sensors reduce power requirements through template-based optical convolution, and they enable a wide field-of-view within a small form through a novel optical design. We describe the trade-offs between the field of view, volume, and mass of these sensors and we provide analytic tools to navigate the design space. We also demonstrate milli-scale prototypes for computer vision tasks such as locating edges, tracking targets, and detecting faces.

Robot audition, which aims at multiple simultaneous human-robot communications in noisy environments, has undergone rapid development since last decade. For a common scenario, robot audition systems usually generate a huge amount of chronological data. These data fall into several categories: sound source locations and orientations, separated sound sources and recognized speeches. In order to efficiently assist people in understanding and analyzing the scenario, it is necessary to organize the data in a delicate way. To achieve this goal, we suggest Speech Arrow which conveys recognized speeches in animated arrow shaped frames. Speech Arrow is highly integrated, intuitive and vivid. In this paper, a single pane auditory scene visualizer is implemented to support Speech Arrow. The visualizer is 3D real-time virtual reality system based on the open source robot audition system HARK. It includes two versions, one of which restores original auditory scenes with offline raw data. The other analyzes and displays online instant data without any delay to help hearing impaired people. We demonstrate that our visualizer improves auditory awareness considerably with experiments.

Near-earth environments are time consuming, labor intensive and possibly dangerous to safe guard. Accomplishing tasks like bomb detection, search-and-rescue and reconnaissance with aerial robots could save resources. This work describes the adoption of insect behavior and flight patterns to develop a MAV sensor suite. A prototype called CQAR: closed quarter aerial robot, which is capable of flying in and around buildings, through tunnels and in and out of caves is used to validate the efficiency of such a method when equipped with optic flow microsensors.

This paper describes a research effort to develop integrated RF sensors for various electronic warfare applications. The immediate application is to enable a micro air vehicle (MAV) to detect and home in on a radar or other RF emitter. Thus the sensors must be able to detect RF signals in frequency bands of interest, identify RF emitters of interest, and provide bearing information to the selected RF emitter. The sensor must weigh no more than several grams in order to fly aboard an MAV. To fabricate such sensors, we are developing compact circuits to detect wide-band RF signals, to identify RF emitters, and to perform direction finding (DF). We are implementing such sensors in CMOS to keep production costs low.

This paper presents a method for generating robust measurements by fusing the outputs of multiple elementary motion detector (EMD) circuits. The competitive feature tracker (CFT) algorithm is used to implement the EMD circuits. CFT measures motion by timing the movement of a feature across the visual field. Fusion is performed with leaky integrators. Each leaky integrator corresponds to a range of velocities and is stimulated whenever an EMD detects motion in the leaky integrator's velocity range. Thus the leaky integrator corresponding to the correct velocity receives the most stimulus. The other leaky integrators receive stimulus only from incorrect measurements. These incorrect stimuli are hence "voted out" and ignored. A proof-of-principle was obtained by implementing the algorithm in an optic flow sensor and exposing the sensor to moving texture stimulus. This sensor was then mounted onto a glider and placed in a control loop to cause the glider to steer away from a wall before collision.