Roman Kuc

Physically Based Simulation Model for Acoustic Sensor Robot Navigation

A computer model is described that combines concepts from the fields of acoustics, linear system theory, and digital signal processing to simulate an acoustic sensor navigation system using time-of-flight ranging. By separating the transmitter/receiver into separate components and assuming mirror-like reflectors, closed-form solutions for the reflections from corners, edges, and walls are determined as a function of transducer size, location, and orientation. A floor plan consisting of corners, walls, and edges is efficiently encoded to indicate which of these elements contribute to a particular pulse-echo response. Sonar maps produced by transducers having different resonant frequencies and transmitted pulse waveforms can then be simulated efficiently. Examples of simulated sonar maps of two floor plans illustrate the performance of the model. Actual sonar maps are presented to verify the simulation results.

Mobile robot sonar for target localization and classification

A novel sonar array is presented that has applications in mobile robotics for localization and mapping of indoor en vironments. The ultrasonic sensor localizes and classifies multiple targets in two dimensions to ranges of up to 8m. By accounting for effects of temperature and humidity, the sys tem is accurate to within 1 mm and 0.1° in still air. Targets separated by 10 mm in range can be discriminated. The error covariance matrix for these measurements is derived to allow fusion with other sensors. Targets are statistically classified into four reflector types: planes, corners, edges, and unknown. This article establishes that two transmitters and two re ceivers are necessary and sufficient to distinguish planes, corners, and edges. A sensor array is presented with this minimum number of transmitters and receivers. A novel de sign approach is used such that the receivers are closely spaced so as to minimize the correspondence problem of as sociating different receiver echoes from multiple targets. A linear filter model for pulse transmission, reception, air absorption, and dispersion is used to generate a set of tem plates for the echo as a function of range and bearing angle. The optimal echo arrival time is estimated from the maximum cross-correlation of the echo with the templates. The use of templates also allows overlapping echoes and disturbances to be rejected. Noise characteristics are modeled for use in the maximum likelihood estimates of target range and bearing. Experimental results are presented to verify assumptions and characterize the sensor.

Differentiating sonar reflections from corners and planes by employing an intelligent sensor

A multitransducer, pulse/echo-ranging system is described that differentiates corner and plane reflectors by exploiting the physical properties of sound propagation. The amplitudes and ranges of reflected signals for the different transmitter and receiver pairs are processed to determine whether the reflecting object is a plane or a right-angle corner. In addition, the angle of inclination of the reflector with respect to the transducer orientation can be measured. Reflected signal amplitude and range values, as functions of inclination angle, provide the motivation for the differentiation algorithm. A system using two Polaroid transducers is described that correctly discriminates between corners and planes for inclination angles within +or-10 degrees of the transducer orientation. The two-transducer system is extended to a multitransducer array, allowing the system to operate over an extended range. An analysis comparing processing effort to estimation accuracy is performed. >

Building a sonar map in a specular environment using a single mobile sensor

The physical properties of acoustic sensors are exploited to obtain information about the environment for sonar map building. A theoretical formulation for interpreting the sensor databases on the physical principles of acoustic propagation and reflection is presented. A characterization of the sonar scan that allows the differentiation of planes, corners, and edges in a specular environment is described. A single sensor mounted on an autonomous vehicle in a laboratory verifies the technique. The implications for sonar map building and the limitations of differentiating elements with one sensor are discussed. >

Clinical Application of an Ultrasound Attenuation Coefficient Estimation Technique for Liver Pathology Characterization

Certain diffuse liver diseases, such as hepatitis and cirrhosis, are difficult to diagnose from the information contained in ultrasound images. A signal processing procedure is described which estimated the value of an acoustic attenuation coefficient of the liver in vivo from reflected ultrasound signals. The results of a clinical trial involving 14 patients indicate a correlation between the coefficient values and the liver diseases: inflamed livers produced lower values than cirrhotic livers. A series of 15 examinations on a volunteer indicates that the observed values are repeatable to within the statistical variation predicted by the mathematical model.

A bat-like sonar system for obstacle localization

An active wide-beam sonar system that mimics the sensor configuration of echolocating bats is described for applications in sensor-based robotics. Obstacles in a two-dimensional (2-D) environment are detected and localized using time-of-flight (TOF) measurements of their echoes. The standard threshold detector produces a biased TOF estimate. An unbiased TOF estimate is derived by a parametric fit to the echo waveform, motivated by experimental observations of actual sonar signals. This novel method forms a tradeoff between the complexity of the optimum estimator and the biased threshold detector. Using the TOF information from both methods, the range and azimuth of an obstacle are estimated. Localization is most accurate if the obstacle is located along the system line-of-sight and improves with decreasing range. Standard deviations of the range and azimuth estimators are compared to the Cramer-Rao lower bounds. The parabolic fit method has large variance but zero bias at large deviations from the line-of-sight. The system operation is generalized from isolated obstacles to extended obstacles. >

An optimal sonar array for target localization and classification

A novel sonar array for mobile robots is presented with applications to localization and mapping of indoor environments. The ultrasonic sensor localizes and classifies multiple targets in two dimensions to ranges of up to 8 meters. By accounting for effects of temperature and humidity, the system is accurate to within 1 mm and 6.1 degrees in still air. Targets separated by 10 mm can be discriminated. Targets are classified into planes, corners, edges and unknown, with the minimum of two transmitters and two receivers. A novel approach is that receivers are closely spaced to minimize the correspondence problem of associating echoes from multiple targets. A set of templates is generated for echoes to allow the optimal arrival time to be estimated, and overlapping echoes and disturbances to be rejected.<<ETX>>

A spatial sampling criterion for sonar obstacle detection

A spatial sampling criterion for sonar systems that allows all obstacles within a given radius from the sensor to be detected is described. The environment considered is a two-dimensional floor plan that is extended into the third dimension, in which the scanning is performed in the horizontal plane. In this environment, edgelike reflectors, such as edges of doors or doorways, and oblique surfaces are the most difficult to detect. By considering the physics of sound propagation, the sonar scanning density required to detect these objects is determined. An experimental verification is included. The limitations of detecting objects with sonar in a more general environment are discussed. These results can be used to determine the necessary spacing in a transducer ring array and the maximum step size that a mobile robot can translate without danger of collision. >

A physically based navigation strategy for sonar-guided vehicles

A novel navigation strategy is described that employs a time- of-flight sonar system to guide an autonomous vehicle through an unstructured environment composed of specular surfaces. Collisions are avoided by employing a scanning procedure that exploits the physics of sound propagation to detect objects. Physical models for the transducer beam and the reflection process are described. These models provide understanding about sonar ranging and are applied to deter mine the necessary scanning procedure for obstacle detection. In our environment, two types of echoes are observed: those reflected from surfaces and those diffracted from edges. Acoustic pulses reflected from oblique surfaces are not usually detected, and diffraction echoes are small and decrease with distance, making them difficult to detect. A scanning proce dure is developed to detect both types of obstacles. This pro cedure is employed to develop a navigation strategy that indicates the perceived obstacle-free region that guarantees that no collision will occur. The approach is illustrated with results produced by a vehicular robot equipped with a Polaroid sensor.

Foliage echoes: A probe into the ecological acoustics of bat echolocation

The research reported here aims at understanding the biosonar system of bats based on the properties of its natural inputs (ecological acoustics). Echoes from foliages are studied as examples of ubiquitous, natural targets. The echo properties and their qualitative relationship to plant architecture are described. The echoes were found to be profoundly stochastic and in general neither Gaussian nor stationary. Consequently, features useful for discrimination of such target classes will be confined to estimated random process parameters. Several such statistical signal features which are sufficiently invariant to allow a classification of the used example plants were identified: the characteristic exponent and the dispersion of an alpha-stable model for the amplitude distribution, a crest factor defined as the ratio of maximum squared amplitude and signal energy, the dispersion of the first threshold passage distribution, the structure of the correlation matrix, and a nonstationarity in sound channel gain. Discrimination error probability could be reduced by combining features pairwise. The best combination was the crest factor and the correlation coefficient of a log-linear model of the time-variant sound channel gain; it yielded an estimated Bayes risk of 6.9% for data pooled from different views.