An Optical Rangefinder for Autonomous Robot Cart Navigation

Miller, G. L.; Wagner, E. R.

doi:10.1007/978-1-4613-8997-2_11

Cited by 14 publications

(7 citation statements)

References 3 publications

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“…The above model could then be used with as much received data as possible. Nitzan et al (1977), Hebert and Krotkov (1991), and Miller and Wagner (1987) report in their work that the received signal strength has a very large dynamic range of optical intensity. This can span several thousands to one.…”

Section: Surface Reflectance Sensitivitymentioning

confidence: 96%

“…This noise term is made up primarily of shot noise from the photodiode and thermal noise from the resistors used in the electronic circuits.2 At the phase comparator the range is estimated from the phase difference shown in Figure 2. The relationship between the standard deviation in the absolute phase of the received signal (J ø to the combined electronic and photon noise standard deviation atotai received by the receiver electronics can be seen graphically as a noise triangle (Miller and Wagner, 1987), which, from Figure 2, gives where a4Wt3 ~ z~ -Q is the magnitude of the change of received signal voltage with phase at the zero crossing (i.e., when the wave cuts the lot axis). From equations (4) and (5), Adams (1992) shows that an approximate relationship between the range variance < &OElig;; > and the received signal amplitude Or is given by where A is the modulation wavelength, < ~n > is the total electronic noise variance, and < &OElig;; > is the variance of any noise sources introduced after phase comparison.…”

Section: Noise Propagation Within the Sensormentioning

confidence: 99%

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The Interpretation of Phase and Intensity Data from AMCW Light Detection Sensors for Reliable Ranging

Adams

Probert

1996

The International Journal of Robotics Research

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The analysis of sensor range data and its application to mobile robot navigation are of crucial importance in the field of mobile robotic research.We analyze the range data produced by an amplitudemodulated continuous wave (AMCW) light detection and ranging sensor and show that by physically modeling such sensors, we not only can produce reliable range estimates, but can also quantify our certainty in each range data point. We discuss the noise in the sensor and show the importance of using both phase and intensity data for calibration and data interpretation.We consider in detail the phenomenon of "mixed pixel points" whereby false range measurements occur when the light beam transmitted is split between two or more surfaces of differing range and/or reflectivity. We describe a new algorithm capable of detecting sudden changes in surface reflectance and/or range in order to identify these "spurious" data points. We quantify the regions over which the detection method will work, as we consider its sensitivity to changes in range and surface reflectance, while also quantifying the possibility of falsely predicting a discontinuity.

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Section: Surface Reflectance Sensitivitymentioning

confidence: 96%

Section: Noise Propagation Within the Sensormentioning

confidence: 99%

The Interpretation of Phase and Intensity Data from AMCW Light Detection Sensors for Reliable Ranging

Adams

Probert

1996

The International Journal of Robotics Research

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“…Finally, Chapter 7 concludes this work by assessing sonar's potential in comparison with other sensors, such as the phase-based infrared rangefinder [100], and discussing some topics for further research.…”

Section: Overviewmentioning

confidence: 99%

Directed Sonar Sensing for Mobile Robot Navigation

1992

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“…Matthies and Shafer (1987) studied the path recognition problem by using a stereo camera. Miller and Wagner (1987) investigated the path generation algorithm that takes in the input of an infrared sensor using a rotating sensor platform. These papers have dealt with the issue of reducing the numbers of sensors, with the algorithms focused on path recognition by using complicated hardware setups.…”

Section: Introductionmentioning

confidence: 99%

A path generation algorithm of an Automatic Guided Vehicle using sensor scanning method

Park

Ahn

Han

2002

KSME International Journal

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In this paper, a path generation algorithm that uses sensor scannings is described. A scanning algorithm for recognizing the ambient environment of the Automatic Guided Vehicle (AGV) that uses the information from the sensor platform is proposed. An algorithm for computing the real path and obstacle length is developed by using a scanning method that controls rotating of the sensors on the platform. The AGV can recognize the given path by adopting this algorithm. As the AG V with two-wheel drive constitute a nonholonomic system, a linearized kinematic model is applied to the AGV motor control. An optimal controller is designed for tracking the reference path which is generated by recognizing the path pattern. Based on experimental results, the proposed algorithm that uses scanning with a sensor platform employing only a small number of sensors and a low cost controller for the AG V is shown to be adequate for path generation.

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An Optical Rangefinder for Autonomous Robot Cart Navigation

Cited by 14 publications

References 3 publications

The Interpretation of Phase and Intensity Data from AMCW Light Detection Sensors for Reliable Ranging

The Interpretation of Phase and Intensity Data from AMCW Light Detection Sensors for Reliable Ranging

Directed Sonar Sensing for Mobile Robot Navigation

A path generation algorithm of an Automatic Guided Vehicle using sensor scanning method

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