RI 9646 REPORT OF INVESTIGATIONS/1998

Concluding Evaluation of a Continuous

Haulage Guidance Sensor

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Public Health Service

Centers for Disease Control and Prevention

National Institute for Occupational Safety and Health
Report of Investigations 9646

Concluding Evaluation of a Continuous

Haulage Guidance Sensor

John J. Sammarco

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Public Health Service

Centers for Disease Control and Prevention

National Institute for Occupational Safety and Health

Pittsburgh Research Laboratory

Pittsburgh, PA

November 1998
International Standard Serial Number

ISSN 1066-5552
CONTENTS

Page

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Introduction ................................................................................... 2

Background ................................................................................... 2

Current operating scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Proposed operating scenario ...................................................................... 3

Requirements analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Environmental requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Operational requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Potential systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Candidate system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Static tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Dynamic tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Static test results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Dynamic test results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Target loss tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Accuracy analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Other mining applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Appendix A.—DynaSight technical specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Appendix B.—Machine tram characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Appendix C.—Serial communication details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

ILLUSTRATIONS

1. Room-and-pillar mining chevron pattern ....................................................... 3

2. Loading zone for the continuous mining and haulage machines ..................................... 4

3. Crosscut parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

4. The DynaSight sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

5. Sensor and active target range of orientation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

6. Dynamic testing setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

7. Raw and corrected data for a single active target oriented at 45b and without the optical window . . . . . . . . . . 10

8. Testing in airborne dust showed less than 0.8% accuracy (worst case) degradation compared with accuracies

at 0 mg/m3

 for a single active target . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

9. Target loss for sump and shear . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

10. Along-track comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

11. Sump distance accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

C-1. Communications block diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

C-2. Sensor's coordinate system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

TABLES

1. Error data using the multitarget adapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
UNIT OF MEASURE ABBREVIATIONS USED IN THIS REPORT

A ampere mm millimeter

cm centimeter m/min meter per minute

ft foot ppm part per million

g gram s second

Hz hertz V ac volt, alternating current

in inch % percent

m meter b degree

mg/m3 milligram per cubic meter

Mention of any company name or product does not constitute endorsement by the National Institute for Occupational Safety and

Health.

To receive additional information about mining issues or other occupational safety and health problems, call 1-800-35-NIOSH

(1-800-356-4674), or visit the NIOSH Home Page on the World Wide Web at http://www.cdc.gov/niosh
CONCLUDING EVALUATION OF A CONTINUOUS

HAULAGE GUIDANCE SENSOR

By John J. Sammarco1

ABSTRACT

The mining industry has the highest average annual fatality rate among major U.S. industries. To address

this, the National Institute for Occupational Safety and Health, Pittsburgh Research Laboratory (PRL), is

conducting major research programs to reduce the hazard exposure of miners. One of the recently concluded

programs at PRL developed the enabling technology for remote-controlled mining. An application involves

the manual process of extracting and hauling coal where operators, in the tight confines of a mine, can be

struck or caught by mobile machinery. The approach to remedy this problem uses a guidance system on the

continuous haulage equipment so that it follows the continuous mining machine that extracts coal. This, in

essence, involves sensor-based docking of the machines. Sensors functioning in the hostile mine environment

of dust, methane gas, and water play the key role. Computer analysis of the mining machine's movements and

empirical machine characterizations established operating requirements and spatial limitations to ensure proper

loading of coal into the haulage equipment. These data served in the selection of a guidance sensor.

Technologies such as scanning laser systems and ultrasonic sensors have frequently been used in other

applications, but were found unacceptable. However, a near-infrared sensor employing active targets met the

requirements. The sensor has a nominal 75b conical field of view and a range from 0.1 to 18.0 m. For the

single-target mode, nominal range accuracy was 4.3% at a distance of 3.65 m. Correction algorithms were

generated, which reduced the error to 0.6%. Airborne dust testing showed less than an additional

0.8% accuracy (worst case) degradation at dust levels up to 15 mg/m3

. Using four targets, the nominal range

accuracy was 0.4% without correction algorithms. Analysis of dynamic testing of a continuous miner and

haulage showed that accuracy was maintained and total target loss did not occur. A guidance system for the

haulage system to follow the mining machine does not exist commercially. Such a system can reduce fatalities,

injuries, and exposure to dust and noise.

1

Electrical engineer, Pittsburgh Research Laboratory, National Institute for Occupational Safety and Health, Pittsburgh, PA.
2

INTRODUCTION

Coal-fired generation plants provide about 60% of U.S.

electricity, thus making the mining industry a vital part of our

national interests. The mining of this coal is quite dangerous

for workers. The mining industry has the highest annual

average fatality rate among major U.S. industries, totaling 31.9

per 100,000 workers [NIOSH 1993].

To address this situation, a program was initiated to develop

the enabling technology for a reduced exposure mining system

(REMS) [McClelland et al. 1994].2

 The objectives were to

reduce hazard exposure and improve the safety and health of

miners working at the face, the most hazardous area of the

mine. With REMS, sensor and computer technology enables

operators to be placed at a safe distance from the working face.

REMS research was concluded with an open industry briefing

in September 1997.

BACKGROUND

REMS targets underground coal mining, specifically,

 room-and-pillar methods. Researchers integrated the extraction

and haulage processes so that the coal can be properly loaded

from the continuous mining machine into the continuous

haulage system (CHS). This task requires the CHS to maintain

a critical position and orientation as it follows the continuous

miner (CM).

Positioning mobile equipment is not unique to mining; it is

a generalized problem [Borenstein et al. 1995]. Sandia National

Laboratories conducted research on sensor-based docking of

large payloads [Drotning 1992]. Sensor-based programmable

vehicles are used for the transport of hazardous materials and

for operation in hazardous environments. Other applications

involve military ground vehicle operations [Gage and Pletter

1987], where sensor-based vehicles follow a lead vehicle.

CURRENT OPERATING SCENARIO

Coal mining utilizes specialized methods and machines.

A common method is known as "room-and-pillar" mining, in

which tunnels are cut according to a predefined manner, such as

a rectangular grid or a chevron pattern. Figure 1 depicts a small

section of a chevron pattern and the equipment used for mining.

The main tunnels are "main entries"; the side tunnels are

"crosscuts." Main entries are 5.5 to 6.0 m wide; crosscuts are

typically 5.5 m. The main entry distances between crosscuts are

typically 18.2 m. The major stages of operation are initial

alignment, advancing the face, turning crosscuts, and backing

out of an entry.

1. Initial alignment: The CHS operator manually positions

and controls the CHS behind the tail end of the CM by using a

remote-control pendant. The objective is to have the tail end of

the conveyor placed directly over the hopper car of the CHS,

thus ensuring proper loading of coal.

2. Advancement: The operator, using a remote-control

pendant, controls the CM to cut coal and advance the main

entries and crosscuts. In developing the main entries, the CM

makes two cuts to obtain the desired width. Crosscuts are

developed after the main entries. During advancement of main

entries and crosscuts, the CHS operator's job is to maintain the

position of the hopper car under the CM's conveyor.

3. Turning a crosscut: The CM begins in the main

entryway. To develop crosscuts, a considerable amount of

positioning and maneuvering is needed because this involves a

large machine in a tightly confined space. A typical "footprint"

of the CM is 3.3 m by 6.0 m. Also, movement is restricted

because the CHS follows closely behind.

4. Backout: The CM and CHS are backed out to begin the

next set of cuts. The primary objective is to ensure that the two

machines do not collide as they back out. The operators must

work together to prevent this.

The CHS used in our research is a Joy 3FCT-4 machine. It

is a flexible belt system that winds its way through the mine

under control of an operator. The tail end discharges coal onto

the section or main belt while the receiving end, called the

hopper, is guided by the operator to follow the rear of the CM.

Operators are in the dangerous area where the roof could fall

and where they can be struck or pinned by moving machinery.

This situation is made more dangerous because visibility is

reduced by dust, obstructions, and low levels of light.

Significant levels of noise, especially during the cutting of coal,

can impair hearing and communication between the operators.

2

This research originated under the former U.S. Bureau of Mines prior to

transferring to the National Institute for Occupational Safety and Health in

1996.
3

Figure 1.—Room-and-pillar mining chevron pattern.

PROPOSED OPERATING SCENARIO

The proposed operating scenario employs a hybrid approach

combining manual and computer-assisted control. Initial

machine alignment is done by manual line-of-sight control

because much maneuvering is needed and this is the most

effective approach. Remote pendants for the CHS and CM are

used for this stage. Manual line-of-sight alignment also enables

visual inspection of the machines before operation from a

remote location, such as a control room. The exposures to

hazards are fewer at this stage because the machine is not

cutting coal; thus, dust and noise are minimal. The operator is

still exposed to potential roof and rib falls during manual

alignment; however, total exposure time has been reduced. The

proposed scenario has electrical cables and water hoses tethered

between the CM and CHS.

The second part of this hybrid control uses computer and

sensor technology for proper positioning of the CHS and for

assisting in control of the CM. A sensing system is needed to

measure the relative x, y, and z position and yaw of the hopper

car with respect to the CM. For reference, the coordinate

system for mining is depicted in figure 2 where y is forward,

x is at a right angle to y, and z is up. Yaw is the horizontal

angle referenced to y.
4

Figure 2.—Loading zone for the continuous mining and haulage machines.
5

Figure 3.—Crosscut parameters.

REQUIREMENTS ANALYSIS

Foremost is the need to determine the machine's position and

orientations, which presents a challenging problem because

underground mining has a dynamic environment of potentially

explosive atmospheres and changing physical configurations as

new areas are mined. Changes in the physical layout may limit

measurement ranges, and the presence of dust may degrade

measurement accuracy. Sensors must not only provide accurate

measurement data, but also operate safely and reliably in the

mine.

ENVIRONMENTAL REQUIREMENTS

1. Dust: Airborne dust concentrations vary in relation to

the particular mining task. Federal law, mandated by the Mine

Safety and Health Administration, sets a maximum level of

2 mg/m3

 for human exposure.

2. Moisture: The sensor will be subjected to water from

splashing or dripping from the roof. Relative humidity can

exceed 95%.

3. Explosive atmosphere: Methane gas is often present in

mines along with airborne coal dust. This mixture can present

an explosive atmosphere depending on the fuel-to-air ratio and

is ignitible by electrical and thermal energy. Therefore, the

sensor must be of intrinsically safe design or made permissible.

OPERATIONAL REQUIREMENTS

Computer analysis of the movements of the CM and CHS

and empirical machine characterizations established operating

requirements and spatial constraints.

Each operational stage needs x, y, and yaw data. Additionally, z data are of use for special situations. For example,

it is expected that the CM and CHS will be separated beyond

the loading zone of figure 2 during the mining process. Z data

become useful to detect when the conveyor height of the CM is

below the top of the hopper car to avoid ramming the CM's

conveyor into the hopper car when returning the machines to the

loading zone. Uneven floors could cause this situation; under

ideal conditions, the maximum adjustable vertical clearance

between the hopper car and conveyor is only 22.9 cm.

The general operational needs are that (1) the relative

positions of the machines are maintained within the loading

zone during coal cutting and (2) the tethered cables between the

machines are not overextended. Operation involves advancement, backout, and turning a crosscut.

1. Advancement: Figure 2 depicts the loading zone for the

two vehicles. A 15.2-cm trajectory of coal from the end of the

conveyor is used as a nominal value. Empirically, the conveyor

full-scale swing is 86b and the maximum separation (y) between

machines is 121.9 cm (given x, z, and yaw = 0), with

the conveyor positioned fully up. A minimum separation of

18 cm provides a "cushion," thus preventing contact between

the machines.

2. Backout: The most critical factor for backout is to ensure

that the machine separation distance does not damage the

tethered power cable. The maximum separation is 3.65 m.

Exceeding this is possible because the machines move at

different rates. The CHS travels at 19.5 m/min in reverse

versus 16.2 m/min for the CM. When backing out of a 12.2-m

section, three to four stops of the CHS are needed to avoid

exceeding maximum separation.

3. Turning a crosscut: This is a very difficult operation to

coordinate between the machines. Analysis of such a cut shows

a maximum yaw of the CHS angle between the machines of

41b. Secondly, the maximum length of cable between the two

machines is 3.65 m, thus giving a second constraint for turning

the crosscut (see figure 3).
6

POTENTIAL SYSTEMS

Everett [1995] and Borenstein et al. [1995] present overviews of various sensors for positioning mobile vehicles. These

include gyroscopes, radio-frequency (RF) position location

systems, and ultrasonic and optical systems. For example, an

active laser-ring gyroscope-based system called the Modular

Azimuth and Positioning System (MAPS) has been produced by

Honeywell Military Avionics. Prior research [Sammarco 1993]

investigated MAPS for mining applications. Additional development and refinement of the system [Schiffbauer 1996]

shows this to be a viable method of determining machine

position; however, the cost is prohibitive for this application.

Ground-based RF systems exist from Harris and Motorola as

described by Everett [1995] and Borenstein et al. [1995].

Complete system cost ranges from $75,000 to $100,000;

however, the resolution range (0.1 to 0.3 m) is unacceptable for

mining applications. Obviously, satellite-based systems also

will not work for underground mining applications.

Ultrasonic sensors, ubiquitous in industrial applications,

were investigated by the Pittsburgh Research Laboratory (PRL)

for navigation of underground mobile mine equipment

[Strickland and King 1993]. Results were favorable for

distance measurements to mine features such as ribs, corners,

and intersections. The application for underground haulage

guidance differs in the characteristics of the target, which is the

back bumper of the CM. It is a smooth piece of metal, 29.2 cm

high and 195.6 cm long. The bumper's bottom is 38.1 cm high

from ground level. From crosscut analysis, two parameters—

incidence angle and range—become very important in the

selection of an ultrasonic transducer. The maximum incidence

angle is 41b, as seen in figure 3; therefore, the beam angle of

the transducer must exceed this value to receive the echo from

the bumper. Let the beam angle be 45b and target distance

equal 3.41 m. With these parameters, the spot diameter (D) of

the ultrasonic beam is calculated as

D = 2R.tan(0.5 B),

where R = target range

and B = beam angle.

Spot diameter is calculated as 282.49 cm. One quickly

realizes that the large spot diameter overshadows the 29.21-cmhigh bumper, thus causing target recognition problems. The

sensor could possibly read specular reflections from the mine

walls, floor, and ceiling and from the conveyor of the CM.

A commercial device for docking consists of a vehicledocking computer and a vehicle-docking head using ultrasonic

transducers and infrared (IR) transponders. The ultrasonic

beam angle of 15b is much less than the 41b needed during

crosscuts. Therefore, this system is also unacceptable because

an echo will not be returned to the sensor once the incidence

angle exceeds 15b. Other position systems are noted in

Frequently Asked Questions (FAQ) by Dowling [1996], which

lists sensors for measuring three or six degrees of freedom;

the systems include electromagnetics and scanning optical

systems. Electromagnetic devices were found unacceptable

when Sammarco [1990] documented the difficulties with these

devices for a mining machine.

Anderson [1989] investigated a scanning laser system with

passive targets for position measurement of a mining machine.

Favorable accuracy results were obtained; however, there is

concern about the physical robustness of the scanning mirror

mechanism. The sensor, in the haulage application, would be

mounted on either the haulage or the mining machine. Both

machines encounter significant shock and vibration that may

become problematic for a scanning sensor.

DynaSight, a near-IR sensor, was identified as a potential

system. A review of the manufacturer's specifications and subsequent discussions led to in-depth examination.

CANDIDATE SYSTEM

DynaSight is a low-cost, commercial, electro-optic sensor

designed to measure x, y, and z of passive or active target.

Optionally, with an active target adapter (ATA) the sensor can

track multiple active targets. The sensor uses eye-safe IR in the

wavelength of 890 mm and has a nominal 75b conical

field of view (FOV) with the origin at the fiduciary mark on the

sensor's front panel. Optionally, the FOV can be factory set

for 50b.

With passive retro reflective targets, IR light is emitted from

two optical transceivers separated by a known distance at

the front of the sensor's optical head, as shown in figure 4. X,

y, and z data are measured with a single passive target and

sensor. Yaw is calculated with two targets and sensors. The

measurement range is proportional to the target size; a 75-mm

targetenables measurements to 6 m. The orientation of a

passive target is limited to about ±45b due to the target spectral

response. With active targets, IR light is emitted from an active

target and received by the sensor. The useful active target

orientation is ±90b.

The sensor's orientation is ±35b (given a 70b FOV) about

vertical axis through the sensor's fiduciary mark. Therefore, the

sensor and targets can undergo a substantial range of orientation

relative to each other, as seen in figure 5. This is important

because during crosscuts, depicted in figure 3, the sensor and

target will vary in orientation.
7

Figure 4.—The DynaSight sensor.

Figure 5.—Sensor and active target range of orientation.

With the ATA and at least two active targets, calculation of

yaw is possible. More than two targets enable redundancy, but

decreases target update rates. With four targets, the update rate

is 16 Hz for each target, or a 4-Hz total update rate.

Because the mining machines move slowly, this update rate

is acceptable. The maximum CM machine forward speed was

measured at 17.5 m/min when moving in free space on a

concrete floor. Thus, the maximum machine movement

between updates is less than 7.6 cm for the CM and 8.3 cm for

the CHS. Because the minimum separation between both

machines is set to 18 cm, the CHS would receive at least one

position update within this 18-cm "cushion."

1. Sensor diagnostics: Diagnostic data, for passive or active

targets, are given in two forms. First is a visual indication using

a single light-emitting diode (LED); second is a digital status

word embedded within the sensor's RS-232C output.

Diagnostics are given for four conditions: search, coast,

caution, and track. During the search mode, the LED is red.

During the coast mode, the sensor has locked on (found) a

target, but has lost it and is attempting to reacquire it. During

coast, the LED is red. During the caution mode, conditions are

marginal and target loss is imminent. This caution status can be

extremely helpful for the mining application. If target loss is

imminent, the controller of the mining machine can take

alternative action or invoke an orderly stop. During caution, the

LED alternates between red and green. During the track mode,

the signal-to-noise ratio of sensor data is acceptable and target

loss is not imminent. For this mode, the LED is green.

2. Redundancy: An effective means to reduce consequences

of component failure or erroneous data is to use redundancy.

Multiple active targets are used because they are most likely to

be damaged when mounted to the rear bumper of the CHS.

Four active targets are used. This enables detection of

erroneous target data by comparison voting schemes. Total

failure of an active target can be addressed by switching out the

failed target. With four active targets, the position update rate

is 4 Hz, which was shown earlier to be acceptable.

TESTING

Testing of the DynaSight sensor was conducted in the

controlled environments of a laboratory and in a dust gallery at

PRL. Two types of tests were conducted: static and dynamic.

Static tests investigated the feasibility of the technology for

mining applications and focused on accuracy and adaptability

for mining. Dynamic tests subjected the system to actual

operating conditions where machine translations, rotations,

vibration, and random obstructions occur. In both cases, the

sensor was interfaced to a personal computer (PC) for control

and data acquisition.

STATIC TESTS

Laboratory tests were designed to address the following

questions: Should passive or active targets be used, and what

were the associated accuracies? Next, assuming the sensor's

accuracy was sufficient, could the sensor be approvable for

operation in a mine environment of dust and methane? The

sensor could be approvable if mounted in an explosion-proof

enclosure fitted with an optical window. However, would the

optical window impede proper sensor operation and degrade
8

accuracy? Lastly, what would be the effect of airborne dust on

accuracy and system robustness?

1. Passive target tests: The basic equipment setup consisted

of the sensor connected to a PC via RS-232C. The PC ran

software written in the "C" programming language to acquire

sensor data and display the output in real time.

Testing used 22- and 75-mm target sizes at distances of 0.31,

2.44, and 3.65 m. Measurements were taken with targets

perpendicular (a target angle of 0b) to the sensor's optical head.

Next, the target was rotated to 45b and the tests were repeated.

This is of interest because the requirements analysis showed the

targets could be at a 41b incidence angle during crosscuts.

Next, testing was repeated using an optical window needed

for the explosion-proof enclosure. The window was a 1.26-cmthick, fire-polished, borosilicate glass with ground and

chamfered edges. Our intent was to determine accuracy and

range degradation due to the window.

Finally, tests were conducted involving false target rejection

and target obstructions. Multiple sources of false targets exist

in this mining application; however, two were of prime concern

—incandescent lighting and retro reflective tape, both of which

are present on miners' caps and mining machines.

Target obstructions could occur as roof material falls or if

coal is improperly discharged from the conveyor into the

haulage equipment. Of interest was whether and how quickly

the sensor locks on (finds) the actual target rather than the false

targets. Debris could fall within the sensor's FOV, causing a

target obstruction.

2. Active target tests: Testing of the active targets followed

the same format as that for passive targets. The main changes

involved the measurement ranges. The minimum distance was

changed from 0.3 to 0.6 m because the sensor did not provide

reliable measurements for distances less than 0.6 m. The

maximum distance was increased to 4.4 m to accommodate a

distance of 1.2 m from the sensor's mounting location to the

front bumper of the haulage machine. In other words, at a

machine separation of 3.7 m, the target reference point is 4.9 m

from the sensor's fiduciary mark (assuming x, z, and yaw  0).

The first group of tests used a single active target. The next

group used the ATA with four active targets arranged in a linear

array. Spacing was 30.5 cm between each target; thus, the

distance from target 1 to 4 was 91.4 cm. Target measurements

were referenced to the center of the array located 45.7 cm from

the outermost targets.

3. Dust tests: Laboratory tests for a single active target were

repeated within a dust gallery. The sensor was mounted within

a dust-tight enclosure fitted with the borosilicate lens. A dust

mixture was introduced into the gallery at concentrations

ranging from 0 to 15 mg/m3

, the maximum obtainable by the

test apparatus. For each level of dust, the targets were oriented

at 0b, 20b, and 40b relative to the sensor. These orientations

were set for each target distance of 0.6 m, 2.5 m, 3.6 m, and

4.9 m.

DYNAMIC TESTS

These tests emulate a typical in-mine operation where the

sensor must track a CM as it moves, cuts coal, and loads coal

into the CHS. The primary objectives of the tests concern

(1) target loss and (2) accuracy. Target loss can occur from

obstructions or vibration. Obstructions primarily consist of

debris falling from the conveyor and into the sensor's FOV.

Vibration can be severe during the sump and shear cycles of

cutting coal, thus preventing the sensor from locking onto a

target. With regard to accuracy, we are primarily interested in

x, y position. It is anticipated that position accuracy could be

affected by machine vibration, dust during the cutting process,

or debris falling from the conveyor.

The basic test scenario aligns the CHS behind the CM.

Next, the CM is advanced approximately 1.5 m while sumping

and shearing during its normal operations. It is during this

opera-tion that the data are collected for dynamic testing.

1. Test setup: The tests were conducted at PRL. Figure 6

depicts the overall setup. During dynamic tests, the CM cuts a

block of "coalcrete," which is simulated coal consisting of coal,

fly ash, and cement. The sensor was mounted on the front end

of the CHS. Connected to it was a single-board computer that

acquired and processed data. The data were logged for analysis

purposes at a rate of approximately 1.2 Hz. Next, on the back

bumper of the CM, four active IR targets were mounted

colinearly for the sensor to track. Vibration measurements were

made by accelerometers shown in figure 6. To measure

vibration of the sensor, an accelerometer was mounted onto the

sensor mounting plate. To measure vibration of the active

targets, an accelerometer was mounted on the target bracket. To

establish a baseline reference for the position of the CM and

CHS, an electronic automated tracking transit (ATT) was

employed. The system used a prism mounted on the CM and a

prism mounted on the CHS. The ATT automatically tracks the

prism as a machine moves. ATT data are expressed as northing

and easting, which can be translated into along-track and crosstrack data. Also located on the CM was the Honeywell Ore

Recovery and Tunneling Aid (HORTA) [Schiffbauer 1996],

which provides navigation information, such as northing,

easting, yaw, and altitude. The ATT accuracy is ±10 mm ±5

ppm for distance and ±1 second of arc. All of the data collected

was time-stamped, including data from the HORTA, ATT,

DynaSight, and accelerometers.
9

Figure 6.—Dynamic testing setup.
10

 Figure 7.—Raw and corrected data for a single active target

oriented at 45b and without the optical window.

 Figure 8.—Testing in airborne dust showed less than 0.8%

accuracy (worst case) degradation compared with accuracies at

0 mg/m3

 for a single active target. (Percent error is relative to

0 mg/m3

 readings.)

RESULTS

STATIC TEST RESULTS

1. Passive targets: The test results are for a 22-mm target.

Of interest was the sensor's accuracy at varying distances when

the target angle was 0b and 45b. These conditions were

repeated with and without the optical window.

In general, errors increased as distance increased. The

worst-case error was 10 cm at a distance of 3.6 m where the

target was at 0b and without the optical window. It was

expected that errors would increase as the target was rotated or

when the optical window was used. In some cases, such as at

Y  0.6 m, this was true. Overall, no generalized pattern for

errors, dependent on the window or target rotation, was evident

through the range of measurements.

At this point, attention focused on false target recognition.

False target sources, such as a miner's cap lamp, were

introduced into the sensor's FOV while the sensor was locked

on the actual passive target. At distances of 0.6 m, 2.5 m, and

3.6 m, the sensor did not detect the cap lamp. Even if an

obstacle obstructed the target, the sensor would, within 0.3 s,

lock back onto the passive target, not the cap lamp. Tests with

reflective tape on a miner's cap showed that when the target was

obstructed, the sensor would lock onto the false target of

reflective tape. Thus, the passive target was deemed

unacceptable.

2. Single active target results: Our test results show that

target rotation has a minor effect on the accuracies. Again,

error was directly related to y. Given the repeatability of the

errors, a single second-order polynomial was generated for error

correction of y data for target rotations of 0b, 20b, and 45b.

The equation is

Compensation factor  .0003Y2 .9933Y  .7796,

where the goodness of fit measure R2  .996.

Applying this compensation reduced errors significantly. At

3.6 m, the maximum separation between the machines during

advance, the worst-case error decreased from 4.3% to 0.6%, as

seen in figure 7.

3. Dust tests: Dust test results (figure 8) showed a

maximum error increase of less than an additional 0.8% with

respect to readings at 0 mg/m3

 of dust. Note that 15 mg/m3 was

the maximum attained by the test gallery. Dust at the rear

bumper of a CM ranges from 5 to 15 mg/m3

. Dust test results

showed very good accuracy at all dust levels; therefore, airborne

dust is not expected to be problematic. An ac-cumulation of

dust on the sensor's lens was noted at the end of the testing.

Long-term field testing is needed to determine the sensor's

operation as dust accumulates on the lens.

4. Multitarget tests: Next, laboratory tests were conducted

using the ATA and four targets. For the tests, x and z were

fixed at 0.5 cm and 1.2 cm, respectively; y and target yaw were

varied. The data are presented in table 1. Worst cases and best

cases are identified.

Significant accuracy improvements are evident in comparisons of the single-target error of figure 7 and the multitarget

error data of table 1. For comparison, corrected y errors at

3.65 cm (expressed as a percentage of reading) were nominally

0.6% for single-target data; multitarget uncorrected error was

0.2 cm, or 0.4%. Yaw error was nominally 0.67b for the

multitarget data.

DYNAMIC TEST RESULTS

Target Loss Tests

If the targets are continually lost by the sensor, accuracy of

the system becomes irrelevant. Data from at least two targets
11

 Figure 9.—Target loss for sump and shear. (Note: Total

target loss did not occur.)

 Figure 10.—Along-track comparison.

are needed to calculate yaw. Four targets were used to provide

redundancy. Target loss is most likely to occur during sump

and shear operations, when vibration and target obstructions are

most prevalent. Videotape recordings during tests confirmed

this by showing significant amounts of debris falling within the

FOV of the sensor as coal was loaded into the hopper. Analysis

of test data showed that total target loss never occurred. There

were times, however, where one or two of the four targets were

lost, as seen in figure 9; therefore, position and yaw data were

always available. During the test, target loss was infrequent and

occurred for less than 1.2 s. One factor contributing to target

loss is vibration. During this same test period, vibration levels

of the sensor were less than 0.5 g of vibration. The targets

located on the back of the CM, on the other hand, typically

measured from 2.5 to 3 g of vibration during the cutting cycle.

In examining the test results, it seems that vibration is not a

significant contributor to target loss. Inspection of videotapes

of the area between the CHS and CM showed that target loss

was most probably due to large pieces of debris blocking the

target area, as can be seen in the videotape. In summary, total

target loss did not occur, and loss of one or two of the targets

occurred infrequently. Therefore, the DynaSight is able to track

during the sump and shear cycles of the CM.

Table 1.—Error data using the multitarget adapter

Y actual,

 m

Y error, 

cm 

Yaw 

actual, o

 

Yaw

error, o

X error,

cm

Z error,

cm

1.22 1

-10.0 0 -1.7 -2.8 0.3 

1.22 -5.8 20 1

6.9 14.8 -0.7 

1.22 NAp 45 NAp NAp NAp 

2.44 0.5 0 -1.6 0.08 2

0.0 

2.44 0.4 20 4.8 -2.7 0.1 

2.44 3.7 45 2

-1.2 -4.6 0.1 

3.65 2.4 0 -1.6 -1.3 1

0.8 

3.65 2

0.2 20 4.8 -3.7 0.8 

3.65 2.0 45 -1.2 3.8 0.8 

4.87 -1.2 0 -1.7 2

0.0 0.8 

4.87 1.0 20 -1.9 -2.6 0.8 

4.87 1.6 45 -3.5 -4.2 0.8 

 NAp Not applicable.

1

Worst case.

2

Best case.

Accuracy Analysis

The ATT served as the baseline reference, as stated earlier,

and provided x, y position in terms of northing and easting. The

ATT thus provides "absolute" measurements of position. The

DynaSight, on the other hand, provides measurements of

position "relative" to the fiduciary mark on the front of the

sensor. To compare data, the ATT readings were converted to

measurements of cross-track and along-track relative to a

starting position of the mining machine. We can correlate the

sensor and the ATT such that the cross-track and the alongtrack both begin at zero for the initial position of the mining

machine, thus enabling direct comparisons of data. To convert

the northing and easting readings of the ATT, it is necessary to

obtain the mining machine's yaw from HORTA, mounted on the

CM. Therefore, cross-track and along-track were calculated

using these equations:

cross-track  (∆ easting) cos (∆ yaw) (∆ northing) sin (∆

yaw)

along-track  (∆ northing) cos (∆ yaw) (∆ easting) sin (∆

yaw)

As ∆ yaw approaches zero, cross-track approaches the value of

∆ easting and along-track approaches ∆ northing values. The

maximum ∆ yaw measured by HORTA was about 0.15° when

cutting coalcrete.

Figure 10 compares DynaSight and ATT along-track. The

x axis has 14 data points taken during the sump and shear

cycles. During this activity, dust, falling debris (target

obstructions), and machine vibration are at a maximum. As one

can see from the data, the along-track measurements for the

ATT and the DynaSight sensor correlate extremely well. The

maximum deviation was 3.35 cm at data point 14, which

occurred during a 1.3-m separation between the targets and

sensor. The mean error for the DynaSight was 1.68 cm.
12

Figure 11.—Sump distance accuracy.

Evaluation of cross-track error became problematic due to

apparent corruption of ATT data. According to the ATT, the

cross-track changed about 2.4 cm from the beginning of the cut

to the end. This was in stark contrast to DynaSight data of

about 12.2 cm. Therefore, cross-track was measured by

physically profiling the actual cut in the coalcrete block.

A conventional transit was used and resulted in a cross-track

measurement of 6.1 cm. This compared with a cross-track of

7.1 cm from the DynaSight when accounting for cross-track

translation due to changes in yaw. This translation is required

because the ATT target was at a different location on the

machine than that of the DynaSight's targets. Thus, cross-track

translations differ when machine yaw changes.

OTHER MINING APPLICATIONS

The DynaSight sensor can be used for other mining applications in addition to guidance between the CHS and CM.

Two such applications concern the control of sump distance for

a CM. The second application uses the sensor for navigation of

a CM in room-and-pillar or possibly highwall applications in a

push-pull technique of guidance described later.

Control of sump distance or sump depth can be quite important when the CM is using a CHS. It is important that the

belt of the haulage is not overloaded by surges of coal or by

overcapacity. This happens when the sump distance becomes

excessive. Obviously, the deeper sumps mean more coal will be

cut and loaded onto the belt. Overloading the belt can significantly decrease the belt life of the continuous haulage.

When controlling the depth of the sump, two areas are of prime

concern: the accuracy of the sensor and robustness during the

sump conditions.

To determine the sensor's performance in this area, we used

the coalcrete test previously described for dynamic testing of

the sensor. From coalcrete tests, it is possible to determine

sump distance accuracy by comparing the output of the sensor

during the sump conditions to the measurements made by the

ATT. The sump accuracy is shown in figure 11, which

compares sump distance measurement between the DynaSight

and the ATT for eight sump data points. Overall, the sensor

measures the sump distance very well given the maximum error

of 1.2 cm. The sensor consistently measured somewhat less

than the ATT, as seen in data points 1, 2, 4, and 8. Measurements during sump are the most demanding for the sensor due

to vibration and shock, as well as high dust levels and the fall

of debris between the two machines during these stages.

Therefore, robustness of the sensor was investigated during

those conditions by examining target loss data. By inspecting

the target data during the dynamic testing in coalcrete, it can be

seen that the sensor never lost sight of all of the targets;

therefore, the sensor is well suited to measuring sump distance

because of its good accuracy and robustness.

The second application uses the sensor for navigating the

CM in room-and-pillar and highwall applications. To use the

sensor, one would need continuous haulage or some other

structure behind the mining machine such that both have

independent movement. In this configuration, navigation of the

CM and control of the guidance between the two machines

would be done in a push-pull technique. The basic technique is

as follows: Both machines start at known positions and

orientations at the beginning of a cut. As the CM advances, the

DynaSight sensor mounted on the CHS tracks and measures the

position of the CM. These data are then used for controlling the

position and orientation of the CM. Once maximum separation

between the CHS and CM is reached, the haulage is advanced

by guiding the CHS in reference to the stationary CM. Once it

reaches the back end of the CM, the process is repeated. Errors

in this situation would be cumulative. Absolute position with

errors relative to the starting point could be determined by

keeping track of each push-pull cycle. Therefore, by knowing

the original position and keeping track of relative measurements

between each push-pull cycle, it is then possible to determine,

at any given time, the absolute position of either machine

relative to the starting position.

There are numerous advantages to using the sensor in this

manner. First, the sensor can perform multiple functions. As

shown in this report, it can be used to guide the continuous

haulage, to control the sump distance, and to navigate the CMs,

as described in this section. This could represent a very

efficient and cost effective way of achieving guidance for all of

these functions. To determine accuracy of the system for

guiding the CM, more analysis of test data is needed. This

could be achieved through computer simulations of the pushpull technique, or it could be determined empirically. The

analysis should include the accuracy of yaw in order to control

the machine's orientation because x, y position alone does not
13

provide a complete picture of the location of the machine. For

example, yaw information is quite important during a highwall

application where rib thickness must be maintained. The

x position may change very little, but if the orientation of the

machine has changed significantly, the danger in cutting

through the rib to the next entry is quite real, possibly resulting

in a cave-in or roof fall. Therefore, an unsafe condition would

result for workers in retrieving the machines.

FUTURE WORK

The DynaSight sensor holds much potential for applications

in mining, as evidenced from the testing and analysis described

earlier in this report. Long-term field testing in actual mining

conditions is needed. Of interest are sensor operation during

extended cutting periods when machine vibration is maximum

and how well the sensor operates as dust accumulates on the

optical lens and active targets. Also, repackaging the sensor's

optical head as a separate subsystem to provide more mounting

flexibility is desirable. This enables increased spacing between

the optical head transceiver; thus, accuracy would improve.

Eliminating the active target tether would also simplify

installation of the system. Although our research program to

develop the enabling technology for REMS concluded in

September 1997, it is hoped that an equipment vendor or mining

company will complete the work described above.

CONCLUSIONS

The use of the DynaSight sensor for integrating the extraction and haulage process could reduce the potential of

miners being struck or caught by moving machinery. This

requires a system to determine the haulage machine's x, y

position and yaw. The DynaSight sensor, with multiple active

targets, is the best candidate based on accuracy, performance in

airborne dust, false target rejection, and robustness. The

multitarget option for the sensor is the most desirable because

of target redundancy, improved accuracy, and reduced

hardware. Dynamic testing subjected the sensor to machine

translations, rotations, vibration, and random obstructions.

Accuracy degradation and total target loss were not encountered. Thus, at this stage, the system has demonstrated

sufficient robustness and accuracy to warrant long-term field

investigations.

ACKNOWLEDGMENT

The author acknowledges John S. Gbruoski (retired) of the

former U.S. Bureau of Mines and Jon A. Hummer and

Raymond W. Vereneck of SSI Services, Inc., Pittsburgh, PA,

for their contributions in the areas of testing, data collection,

analysis, and general assistance.

REFERENCES

Anderson DL [1989]. Position and heading determination of a continuous

mining machine using an angular position-sensing system. Pittsburgh, PA: U.S.

Department of the Interior, Bureau of Mines, IC 9222.

Borenstein J, Everett HR, and Feng L [1995]. Where am I? Sensors and

methods for autonomous mobile robot positioning. Wellesley, MA: A. K.

Peters, Ltd.

Dowling K [1996]. Robotics frequently asked questions list. World Wide

Web [URLhttp://www.frc.ri.cmu.edu/robotics-faq].

Drotning WD [1992]. Automated waste canister docking and emplacement

using a sensor-based intelligent controller. Albuquerque, NM: Sandia National

Laboratories, Report No. SAND91-0558.

Everett HR [1995]. Sensors for mobile robots: theory and application. A. K.

Peters, Ltd.

Gage D, Pletter JB [1987]. Ground vehicle convoying. International Society for Optical Engineering (SPIE), Vol. 852, Mobile Robots II,

pp. 319-328.

McClelland JJ, Randolph RF, Schnakenberg GH Jr., Fowkes RS [1994].

Safety breakthrough: reduced exposure mining system (REMS). Pittsburgh,

PA: U.S. Department of the Interior, Bureau of Mines, SP 26-94.

NIOSH [1993]. Fatal injuries to workers in the United States, 1980-1989:

a decade of surveillance. Cincinnati, OH: U.S. Department of Health and

Human Services, Public Health Service, Centers for Disease Control and

Prevention, National Institute for Occupational Safety and Health, DHHS

(NIOSH) Publication No. 93-108S.

Sammarco JJ [1993]. Field evaluation of the modular azimuth and

positioning system (MAPS) for a continuous mining machine. Pittsburgh, PA:

U.S. Department of the Interior, Bureau of Mines, IC 9354.

Sammarco JJ [1990]. Mining machine orientation control based on inertial,

gravitational, and magnetic sensors. Pittsburgh, PA: U.S. Department of the

Interior, Bureau of Mines, RI 9326.

Schiffbauer WH [1996]. Navigation and control of continuous mining

systems for coal mining. In: Proceedings of the Institute of Electrical and

Electronics Engineers, Industry Applications Society 31st Annual Meeting.

Vol. 4, pp. 2473-2479.

Strickland WH, King RH [1993]. Characteristics of ultrasonic ranging

sensors in an underground environment. Pittsburgh, PA: U.S. Department of

the Interior, Bureau of Mines, RI 9452.
14

APPENDIX A.—DYNASIGHT TECHNICAL SPECIFICATIONS

Following are the DynaSight technical specifications, as

supplied by the manufacturer.

Size: 8.6 by 14.5 by 3.9 cm (7.3 by 5.7 by 1.5 in)

Operating wave band: Near-IR

Field of view: 50 ft azimuth by 50 ft elevation

Measurement update rate: 64 Hz maximum for passive

targets, 200 Hz maximum for active targets

Lock-on delay: 0.3 s typical

Target operating range: 0.1 to 1.5 m for 7-mm passive

targets, 0.3 to 4 m for 25-mm passive targets, 1 to 6 m for

75-mm passive targets

Active target operating range: 0.1 to 18 m for active targets

Measurement resolution: 0.1 mm cross range typical,1

0.4 mm down range typical1

1

 RMS values for 7-mm target at 80-cm range under normal fluorescent room

lights. Values will vary with operating range and target diameter and, to some

extent, with ambient illumination and target position in the field of regard.

Measurement resolutions of 0.05 mm in three axes are achieved at 40-cm range,

and resolution is significantly reduced at range extremes.

Absolute accuracy: 1 mm cross range typical,1 4 mm down

range typical1

Power requirements: 14 V ac @ .6 A (wall transformer for

115 V ac supplied)

Electrical interface: Dual RS-232C with DB-9 connectors

(cable and DB-25 adapters are supplied)

Default data format: 8 bytes per measurement/target update

X, Y, Z in 16-bit two's complement format 0.05 mm per least

significant bit

Manufacturer: Origin Instruments Corp., 2121 Windchime

Dr., Grand Prairie, TX 75051-4117, phone: (214) 264-7212

Three-dimensional measurements are reported in a Cartesian

coordinate system with origin at a fiducial mark on the

DynaSight sensor.
15

APPENDIX B.—MACHINE CHARACTERIZATIONS

Of primary importance in machine data are the tramming

rates of the CHS and CM. Forward and reverse tram rates were

determined empirically for the JOY 3FCT and 14CM. Three

tests were done for each direction. The first forward test was

measured from a standing start. The remaining tests were

measured once the machine was moving; therefore, start/stop

latencies are not included.

3FCT

Direction

Calculated

 m/min, avg

Forward . . . . . . 19.9

Reverse . . . . . . . 19.5

14CM

Direction

Calculated

 m/min, avg

Forward . . . . . . 17.5

Reverse . . . . . . . 16.3
16

Figure C-1.—Communications block diagram.

APPENDIX C.—SERIAL COMMUNICATIONS DETAILS

Figure C-1 shows the basic communications block diagram

for interfacing between the CM, CHS, and the REMS control

system.

Data Format

Two's complement binary data

8 bits/character

No parity

1 stop bit

19,200 baud

Electrical Connection

RS-485 - half duplex

Twisted pair

Control Commands

A control command is sent to the sensor to query for data or to

restart the sensor. The command begins with an "attention"

(03h), followed by the control command data.

Data query . . Query sensor for x, y, z,

yaw,

 and sensor status.

03h, 11h

Cold boot . . . Cold boot (restart) sensor . . 03h, 52h

Reserved . . . Reserved . . . . . . . . . . . . . . . 03h, 24h

Output Data

Output data are sent from the sensor upon the request of a data

query command. The output is in the form of a 16-bit data

board in two's complement. (Note: X, Y, Z data are measured;

yaw and pitch are calculated.)
17

Figure C-2.—Sensor’s coordinate system.

Data Word Format

The output data also contain sensor diagnostic information, as

detailed under “Sensor Status Data (Diagnostics)" below.

Word No. Output data High byte Low byte

Word 0 . . . Miscellaneous . . . . 10000ttss 10000rrrr

Word 1 . . . X position (cm) . . . xxxxxxxx xxxxxxxx

Word 2 . . . Y position (cm) . . . yyyyyyyy yyyyyyyy

Word 3 . . . Z position (cm) . . . zzzzzzzz zzzzzzzz

Word 4 . . . Yaw (radians) . . . . . hhhhhhhh hhhhhhhh

Word 5 . . . Pitch (radians) . . . . pppppppp pppppppp

Word 0

"tt": Targets in the "track" mode

tt Targets

00 . . . 0 or 1*

01 . . . 2

10 . . . 3

11 . . . 4

*At least two targets are needed in the track mode

 in order to obtain yaw data.

“ss": Sensor Status Data (Diagnostics)

ss Description

00 . . . SEARCH: Search for target.

 Searching for 3 or 4 targets.

01 . . . COAST: Lost target, attempting

 to reacquire. Reacquiring 3 or

 4 targets.

10 . . . CAUTION: Marginal conditions,

 loss is imminent for 3 or 4 

targets.

11 . . . TRACK: Proper tracking. At 

least two targets are in the track 

mode.

When status field is "SEARCH" or "COAST", the last data

values during "TRACK" or "CAUTION" are given.

"rrrr" Reserved. Default value is 0000.

Sensor Coordinate System

The coordinate system is shown in figure C-2.

Target Measurement Reference

Yaw and position data are referenced to the midpoint between

targets 0 and 3 as mounted on the rear bumper of the mining

machine. The coordinates of this reference point will be

determined once the targets are mounted to the bumper.

Sensor Update Rate

Lock-on time = 0.3 s × (n targets)

Total update rate = 32 Hz ÷ (n targets)

For two targets (minimum configuration):

Lock-on time = 0.3 s

Total update rate = 16 Hz

For four targets (maximum configuration):

Lock-on time = 1.2 s

Total update rate = 4 Hz
DHHS (NIOSH) Publication No. 98-161

November 1998

To receive other information about occupational safety and health problems, call

1-800-35-NIOSH (1-800-356-4674), or

visit the NIOSH Home Page on the World Wide Web at

http://www.cdc.gov/niosh
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21
22
23
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25
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To receive other information about occupational safety and health problems, call

1-800-35-NIOSH (1-800-356-4674), or

visit the NIOSH Home Page on the World Wide Web at

http://www.cdc.gov/niosh