Egomotion estimation with optic flow and air velocity sensors

We develop a method that allows a flyer to estimate its own motion (egomotion), the wind velocity, ground slope, and flight height using only inputs from onboard optic flow and air velocity sensors. Our artificial algorithm demonstrates how it could be possible for flying insects to determine their absolute egomotion using their available sensors, namely their eyes and wind sensitive hairs and antennae. Although many behaviors can be performed by only knowing the direction of travel, behavioral experiments indicate that odor tracking insects are able to estimate the wind direction and control their absolute egomotion (i.e., groundspeed). The egomotion estimation method that we have developed, which we call the opto-aeronautic algorithm, is tested in a variety of wind and ground slope conditions using a video recorded flight of a moth tracking a pheromone plume. Over all test cases that we examined, the algorithm achieved a mean absolute error in height of 7% or less. Furthermore, our algorithm is suitable for the navigation of aerial vehicles in environments where signals from the Global Positioning System are unavailable.     

Self-Localization of an Omnidirectional Mobile Robot Based on an Optical Flow Sensor

An omnidirectional mobile robot has the advantage that three degrees of freedom of motion in a 2D plane can be set independently, and it can thus move in arbitrary directions while maintaining the same heading. Dead reckoning is often used for self-localization using onboard sensors in omnidirectional robots, by means of measuring wheel velocities from motor encoder data, as well as in car-like robots. However, omnidirectional mobile robots can easily slip because of the nature of omni-wheels with multiple free rollers, and dead reckoning will not work if even one wheel is not attached to the ground. An odometry method where the data is not affected by wheel slip must be introduced to acquire high quality self-location data for omnidirectional mobile robots. We describe a method to obtain robot ego-motion using camera images and optical flow calculation, i.e., where the camera is used as a velocity sensor. In this paper, a silicon retina vision camera is introduced as a mobile robot sensor, which has a good dynamic range under various lighting conditions. A Field-Programmable Gate Array (FPGA) optical flow circuit for the silicon retina is also developed to measure ego-motion of the mobile robot. The developed optical flow calculation system is introduced into a small omnidirectional mobile robot and evaluation experiments for the mobile robot ego-motion are carried out. In the experiments, the accuracy of self-location by the dead reckoning and optical flow methods are evaluated by comparison using motion capture. The results show that the correct position is obtained by the optical flow sensor rather than by dead reckoning.     

An image-interpolation technique for the computation of optic flow and egomotion

 A technique for measuring the motion of a rigid, textured plane in the frontoparallel plane is developed and tested on synthetic and real image sequences. The parameters of motion – translation in two dimensions, and rotation about a previously unspecified axis perpendicular to the plane – are computed by a single-stage, non-iterative process which interpolates the position of the moving image with respect to a set of reference images. The method can be extended to measure additional parameters of motion, such as expansion or shear. Advantages of the technique are that it does not require tracking of features, measurement of local image velocities or computation of high-order spatial or temporal derivatives of the image. The technique is robust to noise, and it offers a simple, novel way of tackling the ‘aperture’ problem. An application to the computation of robot egomotion is also described. Received: 3 September 1993/Accepted in revised form: 16 April 1994     

Biomimetic vibrissal sensing for robots

Active vibrissal touch can be used to replace or to supplement sensory systems such as computer vision and, therefore, improve the sensory capacity of mobile robots. This paper describes how arrays of whisker-like touch sensors have been incorporated onto mobile robot platforms taking inspiration from biology for their morphology and control. There were two motivations for this work: first, to build a physical platform on which to model, and therefore test, recent neuroethological hypotheses about vibrissal touch; second, to exploit the control strategies and morphology observed in the biological analogue to maximize the quality and quantity of tactile sensory information derived from the artificial whisker array. We describe the design of a new whiskered robot, Shrewbot, endowed with a biomimetic array of individually controlled whiskers and a neuroethologically inspired whisking pattern generation mechanism. We then present results showing how the morphology of the whisker array shapes the sensory surface surrounding the robot's head, and demonstrate the impact of active touch control on the sensory information that can be acquired by the robot. We show that adopting bio-inspired, low latency motor control of the rhythmic motion of the whiskers in response to contact-induced stimuli usefully constrains the sensory range, while also maximizing the number of whisker contacts. The robot experiments also demonstrate that the sensory consequences of active touch control can be usefully investigated in biomimetic robots.     

A novel methodology to reproduce previously recorded six-degree of freedom kinematics on the same diarthrodial joint

The objective of this study was to develop a novel method to more accurately reproduce previously recorded 6-DOF kinematics of the tibia with respect to the femur using robotic technology. Furthermore, the effect of performing only a single or multiple registrations and the effect of robot joint configuration were investigated. A single registration consisted of registering the tibia and femur with respect to the robot at full extension and reproducing all kinematics while multiple registrations consisted of registering the bones at each flexion angle and reproducing only the kinematics of the corresponding flexion angle. Kinematics of the knee in response to an anterior (134 N) and combined internal/external (+/-10 N m) and varus/valgus (+/-5 N m) loads were collected at 0 degrees , 15 degrees , 30 degrees , 60 degrees , and 90 degrees of flexion. A six axes, serial-articulated robotic manipulator (PUMA Model 762) was calibrated and the working volume was reduced to improve the robot's accuracy. The effect of the robot joint configuration was determined by performing single and multiple registrations for three selected configurations. For each robot joint configuration, the accuracy in position of the reproduced kinematics improved after multiple registrations (0.7+/-0.3, 1.2+/-0.5, and 0.9+/-0.2 mm, respectively) when compared to only a single registration (1.3+/-0.9, 2.0+/-1.0, and 1.5+/-0.7 mm, respectively) (p<0.05). The accuracy in position of each robot joint configuration was unique as significant differences were detected between each of the configurations. These data demonstrate that the number of registrations and the robot joint configuration both affect the accuracy of the reproduced kinematics. Therefore, when using robotic technology to reproduce previously recorded kinematics, it may be necessary to perform these analyses for each individual robotic system and for each diarthrodial joint, as different joints will require the robot to be placed in different robot joint configurations.     

Simultaneous Optimization of Robot Structure and Control System Using Evolutionary Algorithm

The simultaneous optimization of a robot structure and control system to realize effective mobility in an outdoor environment is investigated. Recently, various wheeled mechanisms with passive and/or active linkages for outdoor environments have been developed and evaluated. We developed a mobile robot having six active wheels and passive linkage mechanisms, and experimentally verified its maneuverability in an indoor environment. However, there are various obstacles in outdoor environment and the travel ability of a robot thus depends on its mechanical structure and control system.We proposed a method of simultaneously optimizing mobile robot structure and control system using an evolutionary algorithm. Here, a gene expresses the parameters of the structure and control system. A simulated mobile robot and controller are based on these parameters and the behavior of the mobile robot is evaluated for three typical obstacles. From the evaluation results, new genes are created and evaluated repeatedly. The evaluation items are travel distance, travel time, energy consumption, control accuracy, and attitude of the robot.Effective outdoor travel is achieved around the 80th generation, after which, other parameters are optimized until the 300th generation. The optimized gene is able to pass through the three obstacles with low energy consumption, accurate control, and stable attitude.     

基于移动终端的稻田飞虱调查方法

【目的】建立一种基于移动终端的稻田飞虱调查方法,以减轻测报人员劳动强度,提高稻田飞虱调查的客观性,实现稻飞虱调查结果可追溯。【方法】利用Android相机、可伸缩手持杆和装载控制相机APP的Android手机研制了稻田飞虱图像采集仪。在Android开发环境下,利用socket通信和视频编码等技术,实现Android相机的视频采集与编码模块、视频传输模块和相机命令控制模块等。利用Android NDK开发和Java web等技术,实现手机端的视频预览模块、手机控制模块、图像上传模块等。相机实时拍摄的视频将压缩成H.264格式,通过RTSP/RTP协议控制其传输至手机端。手机端通过解压缩,实现实时预览相机所拍摄的视频,并控制相机拍摄水稻茎基部飞虱图像,同时将图像传输到手机端。稻飞虱识别算法部署在云服务器上。手机端可选择稻飞虱图像上传至云服务器,云服务器运行稻飞虱自动识别算法,结果返回至手机端。【结果】基于移动终端的稻田飞虱调查方法利用手机可以实时预览相机拍摄的水稻茎基部飞虱画面,控制相机拍照。云服务器上稻飞虱自动识别算法对图像中的飞虱平均检测率为86.9%,虚警率为11.2%;对稻飞虱各虫态平均检测率为81.7%,虚警率为16.6%。【结论】基于移动终端的稻田飞虱调查方法可以便捷地采集到水稻茎基部飞虱图像,实现稻田飞虱不同虫态的识别与计数。该方法可大大减轻测报人员的劳动量,避免稻飞虱田间调查的主观性,实现稻飞虱田间调查的可追溯。     

A Fast Image Alignment Approach for 2D Classification of Cryo-EM Images Using Spectral Clustering

Three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is a significant technique for recovering the 3D structure of proteins or other biological macromolecules from their two-dimensional (2D) noisy projection images taken from unknown random directions. Class averaging in single-particle cryo-EM is an important procedure for producing high-quality initial 3D structures, where image alignment is a fundamental step. In this paper, an efficient image alignment algorithm using 2D interpolation in the frequency domain of images is proposed to improve the estimation accuracy of alignment parameters of rotation angles and translational shifts between the two projection images, which can obtain subpixel and subangle accuracy. The proposed algorithm firstly uses the Fourier transform of two projection images to calculate a discrete cross-correlation matrix and then performs the 2D interpolation around the maximum value in the cross-correlation matrix. The alignment parameters are directly determined according to the position of the maximum value in the cross-correlation matrix after interpolation. Furthermore, the proposed image alignment algorithm and a spectral clustering algorithm are used to compute class averages for single-particle 3D reconstruction. The proposed image alignment algorithm is firstly tested on a Lena image and two cryo-EM datasets. Results show that the proposed image alignment algorithm can estimate the alignment parameters accurately and efficiently. The proposed method is also used to reconstruct preliminary 3D structures from a simulated cryo-EM dataset and a real cryo-EM dataset and to compare them with RELION. Experimental results show that the proposed method can obtain more high-quality class averages than RELION and can obtain higher reconstruction resolution than RELION even without iteration.     

DNA detection using commercial mobile phones

This study investigates the feasibility of using mobile phones cameras for DNA detection. DNA amplification uses the convective polymerase chain reaction (cPCR) technique due to its simple mechanism, which requires no thermal cycling control. Fluorescence increment analysis and information entropy analysis are employed separately to determine whether the test samples contain target DNA (Positive) or not (Negative). The fluorescence increment method uses the brightness of the captured images before and after DNA amplification to calculate ΔF. ΔF values above a threshold level indicate that the test sample is positive. The information entropy method defines the probability, P(C/X), which indicates whether the fluorescence image tends towards a specific shape. If a DNA template is successfully amplified, the captured fluorescence image should be a perfect circle. P(C/X) provides a threshold of 0.5 to identify a circle and values above 0.5 indicate the test sample is positive. Experimental results show that P(C/X) is more effective than ΔF for determining DNA detection results. The information entropy analysis method is applied to ten mobile phones of three different brands equipped with camera sensors, which have pixel numbers ranging from 120 M to 800 M. The clinical evaluation study (n = 60) for screening hepatitis B virus (HBV) plasmid samples shows that the accuracy rate of all models of mobile phones ranges from 85% to 100%. This illustrates that successful DNA detection can be achieved using the most widely deployed electronic device.     

Using an evolutionary algorithm to determine the parameters of a biologically inspired model of head direction cells

A biologically inspired model of head direction cells is presented and tested on a small mobile robot. Head direction cells (discovered in the brain of rats in 1984) encode the head orientation of their host irrespective of the host’s location in the environment. The head direction system thus acts as a biological compass (though not a magnetic one) for its host. Head direction cells are influenced in different ways by idiothetic (host-centred) and allothetic (not host-centred) cues. The model presented here uses the visual, vestibular and kinesthetic inputs that are simulated by robot sensors. Real robot-sensor data has been used in order to train the model’s artificial neural network connections. The main contribution of this paper lies in the use of an evolutionary algorithm in order to determine the values of parameters that determine the behaviour of the model. More importantly, the objective function of the evolutionary strategy used takes into consideration quantitative biological observations reported in the literature.     

Mobile location estimation using density-based clustering technique for NLoS environments

Mobile location technologies have drawn much attention to cope with the mass demands of wireless communication services. Although clustering spatial data is viewed as an effective way to access the objects located in a physical space, little has been done in estimating mobile location. In wireless communication, one of the main problems with accurate location is nonline of sight (NLoS) propagation. To solve the problem, we present a new location algorithm with clustering technique by utilizing the geometrical feature of cell layout, time of arrival range measurements, and three base stations. The mobile location is estimated by solving the optimal solution of the objective function based on the high density cluster. Furthermore, our proposed algorithm only needs three range measurements and does not distinguish between NLoS and LoS environments. Simulations study was conducted to evaluate the performance of the algorithm for different NLoS error distributions and various upper bound of NLoS error. The results of our experiments demonstrate that the proposed algorithm is significantly more effective in location accuracy than range scaling algorithm, linear lines of position algorithm, and Taylor series algorithm, and also satisfies the location accuracy demand of E-911.

Mobile image sensors for object detection using color segmentation

The intelligent mobile robot with sensors and image processing embedded system combines the suction and aerodynamic attraction to achieve good balance between strong adhesion force and high mobility. Experimental results showed that the robot can move upward on the wall at the speed of 2.9 m/min and carry 5 kg payload in addition to 2.5 kg self-weight, which record the highest payload capacity among climbing robots of similar size. It also implements object detection ability using effective color transform and segmentation technique for the exact target detection on the wall using a embedded camera system, communication module and several active sensors.     

Low impedance walking robots

For both historical and technological reasons, most robots,including those meant to mimic animals or operate in naturalenvironments,³ use actuators and control systems that have high(stiff) mechanical impedance. By contrast, most animals exhibitlow (soft) impedance. While a robot's stiff joints may be programmedto closely imitate the recorded motion of an animal's soft joints,any unexpected position disturbances will generate reactiveforces and torques much higher for the robot than for the animal.The dual of this is also true: while an animal will react toa force disturbance by significantly yielding position, a typicalrobot will greatly resist. These differences cause three deleterious effects for high impedancerobots. First, the higher forces may cause damage to the robotor to its environment (which is particularly important if thatenvironment includes people). Second, the robot must acquirevery precise information about its position relative to theenvironment so as to minimize its velocity upon impact. Third,many of the self-stabilizing effects of natural dynamics are"shorted out"⁴ by the robot's high impedance, so that stabilizationrequires more effort from the control system. Over the past 5 yr, our laboratory has designed a series ofwalking robots based on "Series-Elastic Actuators" and "VirtualModel Control." Using these two techniques, we have been ableto build low-impedance walking robots that are both safe androbust, that operate blindly without any model of upcoming terrain,and that add minimal control effort in parallel to their self-stabilizingpassive dynamics. We have discovered that it is possible toachieve surprisingly effective ambulation from rather simplemechanisms and control systems. After describing the historicaland technological motivations for our approach, this paper givesan overview of our methods and shows some of the results wehave obtained.     

Solving navigational uncertainty using grid cells on robots

To successfully navigate their habitats, many mammals use a combination of two mechanisms, path integration and calibration using landmarks, which together enable them to estimate their location and orientation, or pose. In large natural environments, both these mechanisms are characterized by uncertainty: the path integration process is subject to the accumulation of error, while landmark calibration is limited by perceptual ambiguity. It remains unclear how animals form coherent spatial representations in the presence of such uncertainty. Navigation research using robots has determined that uncertainty can be effectively addressed by maintaining multiple probabilistic estimates of a robot's pose. Here we show how conjunctive grid cells in dorsocaudal medial entorhinal cortex (dMEC) may maintain multiple estimates of pose using a brain-based robot navigation system known as RatSLAM. Based both on rodent spatially-responsive cells and functional engineering principles, the cells at the core of the RatSLAM computational model have similar characteristics to rodent grid cells, which we demonstrate by replicating the seminal Moser experiments. We apply the RatSLAM model to a new experimental paradigm designed to examine the responses of a robot or animal in the presence of perceptual ambiguity. Our computational approach enables us to observe short-term population coding of multiple location hypotheses, a phenomenon which would not be easily observable in rodent recordings. We present behavioral and neural evidence demonstrating that the conjunctive grid cells maintain and propagate multiple estimates of pose, enabling the correct pose estimate to be resolved over time even without uniquely identifying cues. While recent research has focused on the grid-like firing characteristics, accuracy and representational capacity of grid cells, our results identify a possible critical and unique role for conjunctive grid cells in filtering sensory uncertainty. We anticipate our study to be a starting point for animal experiments that test navigation in perceptually ambiguous environments.     

A gene frequency model for QTL mapping using Bayesian inference

Background

Information for mapping of quantitative trait loci (QTL) comes from two sources: linkage disequilibrium (non-random association of allele states) and cosegregation (non-random association of allele origin). Information from LD can be captured by modeling conditional means and variances at the QTL given marker information. Similarly, information from cosegregation can be captured by modeling conditional covariances. Here, we consider a Bayesian model based on gene frequency (BGF) where both conditional means and variances are modeled as a function of the conditional gene frequencies at the QTL. The parameters in this model include these gene frequencies, additive effect of the QTL, its location, and the residual variance. Bayesian methodology was used to estimate these parameters. The priors used were: logit-normal for gene frequencies, normal for the additive effect, uniform for location, and inverse chi-square for the residual variance. Computer simulation was used to compare the power to detect and accuracy to map QTL by this method with those from least squares analysis using a regression model (LSR).

Results

To simplify the analysis, data from unrelated individuals in a purebred population were simulated, where only LD information contributes to map the QTL. LD was simulated in a chromosomal segment of 1 cM with one QTL by random mating in a population of size 500 for 1000 generations and in a population of size 100 for 50 generations. The comparison was studied under a range of conditions, which included SNP density of 0.1, 0.05 or 0.02 cM, sample size of 500 or 1000, and phenotypic variance explained by QTL of 2 or 5%. Both 1 and 2-SNP models were considered. Power to detect the QTL for the BGF, ranged from 0.4 to 0.99, and close or equal to the power of the regression using least squares (LSR). Precision to map QTL position of BGF, quantified by the mean absolute error, ranged from 0.11 to 0.21 cM for BGF, and was better than the precision of LSR, which ranged from 0.12 to 0.25 cM.

Conclusions

In conclusion given a high SNP density, the gene frequency model can be used to map QTL with considerable accuracy even within a 1 cM region.     

Simulator for migration processes and wear in the artificial hip acetabulum for radiologic measurement]

For assessing migration of cups, standard X-rays or stereo radiological images (SRI) are available. In addition, software is also available for measurements. The accuracies of the various systems are established statistically, in part combined with phantoms, and compared. To date, no known phantom is available for the simulation of acetabular cup migration with account being taken of the position of the pelvis in the X-ray beam. Such an appliance covering 8 different parameters has now been developed, the cup can be moved horizontally, vertically and in the loading direction. Angular accuracy is +/- 0.5 degree, and wear of a magnitude of 0.25 mm can be simulated. Two degree elevation of the pelvis, left or right, can be simulated. The position of the pelvis around the horizontal axis permits continuous variation. This appliance can simulate migratory movements of the acetabular cup within a pelvis, and wear within the cup. In addition, the spatial position of the pelvis can be varied. The X-ray images can be used to investigate the accuracy of evaluation strategies.     

Optic flow estimation on trajectories generated by bio-inspired closed-loop flight

We generated panoramic imagery by simulating a fly-like robot carrying an imaging sensor, moving in free flight through a virtual arena bounded by walls, and containing obstructions. Flight was conducted under closed-loop control by a bio-inspired algorithm for visual guidance with feedback signals corresponding to the true optic flow that would be induced on an imager (computed by known kinematics and position of the robot relative to the environment). The robot had dynamics representative of a housefly-sized organism, although simplified to two-degree-of-freedom flight to generate uniaxial (azimuthal) optic flow on the retina in the plane of travel. Surfaces in the environment contained images of natural and man-made scenes that were captured by the moving sensor. Two bio-inspired motion detection algorithms and two computational optic flow estimation algorithms were applied to sequences of image data, and their performance as optic flow estimators was evaluated by estimating the mutual information between outputs and true optic flow in an equatorial section of the visual field. Mutual information for individual estimators at particular locations within the visual field was surprisingly low (less than 1 bit in all cases) and considerably poorer for the bio-inspired algorithms that the man-made computational algorithms. However, mutual information between weighted sums of these signals and comparable sums of the true optic flow showed significant increases for the bio-inspired algorithms, whereas such improvement did not occur for the computational algorithms. Such summation is representative of the spatial integration performed by wide-field motion-sensitive neurons in the third optic ganglia of flies.     

Insect visual homing strategies in a robot with analog processing

 The visual homing abilities of insects can be explained by the snapshot hypothesis. It asserts that an animal is guided to a previously visited location by comparing the current view with a snapshot taken at that location. The average landmark vector (ALV) model is a parsimonious navigation model based on the snapshot hypothesis. According to this model, the target location is unambiguously characterized by a signature vector extracted from the snapshot image. This article provides threefold support for the ALV model by synthetic modeling. First, it was shown that a mobile robot using the ALV model returns to the target location with only small position errors. Second, the behavior of the robot resembled the behavior of bees in some experiments. And third, the ALV model was implemented on the robot in analog hardware. This adds validity to the ALV model, since analog electronic circuits share a number of information-processing principles with biological nervous systems; the analog implementation therefore provides suggestions for how visual homing abilities might be implemented in the insect's brain. Received: 15 June 1999 / Accepted in revised form: 20 March 2000     

Robotic and neuronal simulation of the hippocampus and rat navigation.

The properties of hippocampal place cells are reviewed, with particular attention to the nature of the internal and external signals that support their firing. A neuronal simulation of the firing of place cells in open-field environments of varying shape is presented. This simulation is coupled with an existing model of how place-cell firing can be used to drive navigation, and is tested by implementation as a miniature mobile robot. The sensors on the robot provide visual, odometric and short-range proximity data, which are combined to estimate the distance of the walls of the enclosure from the robot and the robot''s current heading direction. These inputs drive the hippocampal simulation, in which the robot''s location is represented as the firing of place cells. If a goal location is encountered, learning occurs in connections from the concurrently active place cells to a set of ''goal cells'', which guide subsequent navigation, allowing the robot to return to an unmarked location. The system shows good agreement with actual place-cell firing, and makes predictions regarding the firing of cells in the subiculum, the effect of blocking long-term synaptic changes, and the locus of search of rats after deformation of their environment.     

Dynamics parametrically controlled by image correlations organize robot navigation

Behavior-based robot designs confront the problem of how different elementary behaviors can be integrated. We address two aspects of this problem: the stabilization of behavioral decisions that are induced by changing sensory information and the fusion of multiple sources of sensory information. The concrete context is homing and obstacle avoidance in a vision-guided mobile robot. Obstacle avoidance is based on extracting time-to-contact information from optic flow. A dynamical system controls heading direction and velocity. Time-to-contact estimates parametrically control this dynamical system, the attractors of which generate robot movement. Decisions come about through bifurcations of the dynamics and are stabilized through hysteresis. Homing is based on image correlations between memorized and current views. These control parametrically a dynamics of ego-position estimation, which converges in closed loop so as to position the robot at the home position. Unreliable visual information and more continous open-loop dead-reckoning information are integrated within this dynamics. This permits vision-based homing, but also stabilizes the behavior during periods of absent or erroneous visual information through the internal state of the dynamical system. The navigation scheme is demonstrated on a robot platform in real time. Received: 2 May 1995 / Accepted in revised form: 10 June 1996