Wide-field Integration Research Articles

Lidar-Based Navigation of Subterranean Environments Using Bio-Inspired Wide-Field Integration of Nearness.

Navigating unknown environments is an ongoing challenge in robotics. Processing large amounts of sensor data to maintain localization, maps of the environment, and sensible paths can result in high compute loads and lower maximum vehicle speeds. This paper presents a bio-inspired algorithm for efficiently processing depth measurements to achieve fast navigation of unknown subterranean environments. Animals developed efficient sensorimotor convergence approaches, allowing for rapid processing of large numbers of spatially distributed measurements into signals relevant for different behavioral responses necessary to their survival. Using a spatial inner-product to model this sensorimotor convergence principle, environmentally relative states critical to navigation are extracted from spatially distributed depth measurements using derived weighting functions. These states are then applied as feedback to control a simulated quadrotor platform, enabling autonomous navigation in subterranean environments. The resulting outer-loop velocity controller is demonstrated in both a generalized subterranean environment, represented by an infinite cylinder, and nongeneralized environments like tunnels and caves.

Guidelines for practical navigation systems based on wide‐field‐integration of optic flow

AbstractThis paper shows some guidelines to improve the estimation accuracy of a navigation system, which is based on wide‐field‐integration (WFI) of optic flow. Optic flow is a vector field of relative velocities obtained by photoreceptors in image sensors, and WFI of optic flow enables motion estimation robust by integrating wide range of optic flow. Since the system has several attractive features (small size, light weight, and low computation), it is applicable to an autonomous control system of micro air vehicles. However, due to some restrictions of real systems, WFI of optic flow theory should be applied while considering sensor arrangement for better estimation. In this paper, first an adequate number and adequate optical‐axes of image sensors are investigated. Then, a new estimation system combining a gyro sensor with WFI of optic flow is discussed by numerical simulations. Finally, the essential effect for the number of cameras are verified in experiments.

Low-Power, Bio-Inspired Time-Stamp-Based 2-D Optic Flow Sensor for Artificial Compound Eyes of Micro Air Vehicles

We report a low-power, bio-inspired mixed-signal 2-D optic flow sensor to realize artificial compound eyes, which can provide a wide field of view (FoV) sensing capability for autonomous navigation of micro-air-vehicles (MAVs). Inspired by insect vision, a 2-D time-stamp algorithm has been developed to scale the number of deployable sensors at low power. The fabricated sensor estimates 16-b 2-D optic flows of maximum 1.96 rad/s with FoV of 60° from the integrated mixed-signal algorithm core, which consumes only 243.3 pJ/pixel or $\sim 30~\mu \text{W}$ at 120 fps. In addition, the peripheral circuits for modular design have been integrated on chip to provide optic flow data compression, wide-field integration (WFI), and serial peripheral interface (SPI). More than 25 sensors can be connected in a single SPI bus and transmit the full resolution optic flows. The fabricated prototype sensor supports full resolution 2-D optic flows from an array of $64\times 64$ pixels at 120 fps through a 3 MB/s SPI bus (4 wires total).

APCV 2014, 19–22 July 2014, Takamatsu, Japan Abstracts

How are the image statistics of global image contrast computed? We answered this by using a contrast-matching task for checkerboard configurations of ‘battenberg’ micro-patterns where the contrasts and spatial spreads of interdigitated pairs of micro-patterns were adjusted independently. Test stimuli were 20 × 20 arrays with various sized cluster widths, matched to standard patterns of uniform contrast. When one of the test patterns contained a pattern with much higher contrast than the other, that determined global pattern contrast, as in a max() operation. Crucially, however, the full matching functions had a curious intermediate region where low contrast additions for one pattern to intermediate contrasts of the other caused a paradoxical reduction in perceived global contrast. None of the following models predicted this: RMS, energy, linear sum, max, Legge and Foley. However, a gain control model incorporating wide-field integration and suppression of nonlinear contrast responses predicted the results with no free parameters. This model was derived from experiments on summation of contrast at threshold, and masking and summation effects in dipper functions. Those experiments were also inconsistent with the failed models above. Thus, we conclude that our contrast gain control model (Meese & Summers, 2007) describes a fundamental operation in human contrast vision.

Electrolocation-based underwater obstacle avoidance using wide-field integration methods

Weakly electric fish are capable of efficiently performing obstacle avoidance in dark and navigationally challenging aquatic environments using electrosensory information. This sensory modality enables extraction of relevant proximity information about surrounding obstacles by interpretation of perturbations induced to the fish’s self-generated electric field. In this paper, reflexive obstacle avoidance is demonstrated by extracting relative proximity information using spatial decompositions of the perturbation signal, also called an electric image. Electrostatics equations were formulated for mathematically expressing electric images due to a straight tunnel to the electric field generated with a planar electro-sensor model. These equations were further used to design a wide-field integration based static output feedback controller. The controller was implemented in quasi-static simulations for environments with complicated geometries modelled using finite element methods to demonstrate sense and avoid behaviours. The simulation results were confirmed by performing experiments using a computer operated gantry system in environments lined with either conductive or non-conductive objects acting as global stimuli to the field of the electro-sensor. The proposed approach is computationally inexpensive and readily implementable, making underwater autonomous navigation in real-time feasible.

Comparison of Integrated and Nonintegrated Wide-Field Optic Flow for Vehicle Navigation

Recent studies of vision-based navigation and guidance for robotic vehicles have been inspired by the biological systems found in flying insects. The wide-field integration of optic flow is one pre-existing method, in which the sensed optic flow is integrated along with sensitivity functions to mimic the action of directionally sensitive cells observed in some insects’ visual systems. This study re-examines the wide-field integration method and reformulates the problem from a summation rather than an integral. This reformulation allows the wide-field integration measurement outputs to be directly compared with nonintegrated optic flow measurements. The method using nonintegrated optic flow measurements is shown to have some practical advantages, such as eliminating the need to define input sensitivity functions and having a measurement Jacobian that is easier to derive analytically. Also, the state estimates obtained with the nonintegrated method are proven to have minimum variance compared with those from the wide-field integration method. Numerical simulations of each method are shown for a vehicle maintaining level flight at constant altitude over a flat terrain.

1P1-K06 オプティカルフローの広域統合法による宇宙着陸探査機の自律制御(【宇宙工学部門】宇宙工学とロボティクス・メカトロニクス(2))

This paper proposes a method to estimate all state variables of a space probe in a proximity to an asteroid with Wide Field Integration of optic flow. In authors' previous papers, it is assumed that a space probe has an altitude sensor. In this paper, the altitude is estimated from the velocity change between two states before and after thruster firing. For this altitude estimation process, the probe's attitude angle is required. Thus, this paper also addresses a method to evaluate the attitude angle accurately. To demonstrate the validity of the proposed method, a numerical simulation of a spacecraft with two control thrusters is shown.

Analog VLSI Implementation of Wide-field Integration Methods

In this paper a novel integrated, single-chip solution for autonomous navigation inspired by the computations in the insect visuomotor system is proposed. A generalization of the theory of wide field integration (WFI) is presented which supports the use of sensors with a limited field of view, and the system concept is validated based on experiments using a prototype single-chip WFI sensor. The VLSI design implements (1) an array of Elementary Motion Detectors (EMDs) to derive local estimates of optic flow, (2) a novel mismatch compensation approach to handle dissimilarities in local motion detector units, and (3) on-chip programmable optic flow pattern weighting (Wide-Field Integration) to extract relative speed and proximity with respect to the surrounding environment. Computations are performed in the analog domain and in parallel, providing outputs at 1 kHz while consuming only 42.6 μW of power. The resulting sensor is integrated with a ground vehicle and navigation of corridor-like environments is demonstrated.

Bioinspired Visuomotor Convergence

In this paper, wide-field integration methods, which are inspired by the spatial decompositions of wide-field patterns of optic flow in the insect visuomotor system, are explored as an efficient means to extract visual cues for guidance and navigation. A control-theoretic framework is developed and used to quantitatively link weighting functions to behaviorally-relevant interpretations such as relative orientation, position, and speed in a corridor environment. It is shown through analysis and demonstration on a ground vehicle that the proposed sensorimotor architecture gives rise to navigational heuristics, namely, centering and speed regulation, which are observed in natural systems.

Autonomous Navigation in Three-Dimensional Urban Environments Using Wide-Field Integration of Optic Flow

In this paper, a control theoretic framework is introduced to analyze an information extraction approach from patterns of optic flow based on analogs to wide-field motion-sensitive interneurons in the insect visuomotor system. An algebraic model of optic flow is developed, based on a parameterization of three-dimensional urban environments. It is shown that estimates of proximity and speed, relative to these environments, can be extracted using weighted summations of the instantaneous patterns of optic flow. Small perturbation techniques are then applied to link weighting patterns to outputs, which are applied as feedback to facilitate stability augmentation and perform local obstacle avoidance and terrain following. Additive noise and environment uncertainties are incorporated into an offline procedure for determination of optimal weighting functions. Stability is proven via local asymptotic analysis and the resulting approach demonstrated in simulation using a micro helicopter in a three-dimensional urbanlike environment.

Erratum to: Implementation of wide-field integration of optic flow for autonomous quadrotor navigation

– Page 2, last paragraph of Sect. 1. Lines 7 through 13 should read: Section 4 discusses the identification of the vehicle dynamics and the implementation of the optic flow-based wide field integration control strategy. Section 5 describes in detail the closed loop stability of the vehicle during corridor navigation. Section 6 presents results and summarizes the effectiveness of wide field integration navigation using optic flow, as observed in the experimentation. Section 7 summarizes the conclusions. – Page 2. The first paragraph of Sect. 3 should read: In this section a spatially continuous representation of planar optic flow for corridor environments is developed and a model for extracting relative position and orientation based on the L2 inner product is introduced.

SENSORIMOTOR CONVERGENCE IN VISUAL NAVIGATION AND FLIGHT CONTROL SYSTEMS

Insects exhibit unparalleled and incredibly robust flight dynamics in the face of uncertainties. A fundamental principle contributing to this amazing behavior is rapid processing and convergence of visual sensory information to flight motor commands via spatial wide-field integration, accomplished by motion pattern sensitive interneurons in the lobula plate portion of the visual ganglia. Within a control-theoretic framework, a model for wide-field integration of retinal image flow is developed, establishing the connection between image flow kernels (retinal motion pattern sensitivities) and the feedback terms they represent. It is demonstrated that the proposed output feedback methodology is sufficient to give rise to experimentally observed navigational heuristics as the centering and forward speed regulation responses exhibited by honeybees.

A biologically inspired modular VLSI system for visual measurement of self-motion

We introduce a biologically inspired computational architecture for small-field detection and wide-field spatial integration of visual motion based on the general organizing principles of visual motion processing common to organisms from insects to primates. This highly parallel architecture begins with two-dimensional (2-D) image transduction and signal conditioning, performs small-field motion detection with a number of parallel motion arrays, and then spatially integrates the small-field motion units to synthesize units sensitive to complex wide-field patterns of visual motion. We present a theoretical analysis demonstrating the architecture's potential in discrimination of wide-field motion patterns such as those which might be generated by self-motion. A custom VLSI hardware implementation of this architecture is also described, incorporating both analog and digital circuitry. The individual custom VLSI elements are analyzed and characterized, and system-level test results demonstrate the ability of the system to selectively respond to certain motion patterns, such as those that might be encountered in self-motion, at the exclusion of others.