Where am I? Sensors and Methods for Mobile Robot Positioning

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R e t r o r e f l e c t i v e m a r k e r s 178 Part II Systems and Methods for Mobile Robot Positioning The accuracy of the marker correction is much higher (and therefore assigned greater credibility) than that of the lateral sonar readings due to the markedly different uncertainties associated with the respective targets. The polarized Banner sensor re- Figure 7.5: Polarized retroreflective proximity sensors are used to locate vertical strips of retroreflective tape attached to shelving support posts in the Camp Elliott warehouse installation of the MDARS security robot [Everett et al, 1994]. sponds only to the presence of a retroreflector while ignoring even hi ghly specular surrounding surfaces, whereas the ultrasonic energy from the sonar will echo back from any reflective surface encountered by its relatively wide beam. Protruding objects in the vicinity of the tape (quite common in a warehouse environment) result in a shorter measured range value than the reference distance for the marker itself. The overall effect on x-y bias is somewhat averaged out in the long run, as each time the vehicle executes a 90-degree course change the association of x- and y-components with tape versus sonar updates is interchanged. 7.3.2 Caterpillar Self Guided Vehicle Caterpillar Industrial, Inc., Mentor, OH, manufactures a free-ranging AGV for materials handling that relies on a scanning laser triangulation scheme to provide positional updates to the vehicle’s onboard odometry system. The Class-I laser rotates at 2 rpm to illuminate passive retroreflective bar-code targets affixed to walls or support columns at known locations up to 15 meters (50 ft) away [Gould, 1990; Byrne et al., 1992]. The bar-codes serve to positively identify the reference target and eliminate ambiguities due to false returns from other specular surfaces within the operating area. An Figure 7.6: Retroreflective bar-code targets spaced 10 to 15 meters (33 to 49 ft) apart are used by the Caterpillar SGV to triangulate position. (Adapted from [Caterpillar, 1991a].) onboard computer calculates x-y position updates through simple triangulation to null out accumulated odometry errors (see Figure 7.6). Some target occlusion problems have been experienced in exterior applications where there is heavy fog, as would be expected, and minor difficulties have been encountered as well during periods when the sun was low on the horizon [Byrne, 1993]. Caterpillar's Self Guided Vehicle (SGV) relies on dead reckoning under such conditions to reliably continue its route for distances of up to 10 meters (33 ft) before the next valid fix.
Chapter 7: Landmark Navigation 179 The robot platform is a hybrid combination of tricycle and differential drive, employing two independent series-wound DC motors powering 45-centimeter (18 in) rear wheels through sealed gear-boxes [CATERPILLAR, 1991]. High-resolution resolvers attached to the single front wheel continuously monitor steering angle and distance traveled. A pair of mechanically scanned nearinfrared proximity sensors sweeps the path in front of the vehicle for potential obstructions. Additional near infrared sensors monitor the area to either side of the vehicle, while ultrasonic sensors cover the back. 7.3.3 Komatsu Ltd, Z-shaped landmark Metal sensor matsuda2.cdr, .wmf Z-shaped landmark 10 m Aluminum tape 3 m 50 m 50 m Figure 7.7: Komatsu's Z-shaped landmarks are located at 50-meter (164 ft) intervals along the planned path of the autonomous vehicle. (Courtesy of [Matsuda and Yoshikawa, 1989].) 50 m matsuda1.cdr, .wmf Komatsu Ltd. in Tokyo, Japan, is a manufacturer of construction machines. One of Komatsu's research projects aims at developing an unmanned dump truck. As early as 1984, researchers at Komatsu Ltd. developed an unmanned electric car that could follow a previously taught path around the company's premises. The vehicle had two onboard computers, a directional gyrocompass, two incremental encoders on the wheels, and a metal sensor which detected special landmarks along the planned path (see Figure 7.7). The accuracy of the vehicle's dead-reckoning system (gyrocompass and encoders) was approximately two percent on the paved road and during straight-line motion only. The mechanical gyrocompass was originally designed for deep-sea fishing boats and its static direction accuracy was 1 degree. On rough terrain the vehicle's dead-reckoning error deteriorated notably. For example, running over a 40-millimeter (1.5 in) height bump and subsequently traveling along a straight line for 50 meters (164 ft), the vehicle's positioning error was 1.4 m (55 in). However, with the Z-shaped landmarks used in this project for periodic recalibration the positioning could be recalibrated to an accuracy of 10 centimeters (4 in). The 3 meter (118 in) wide landmark was made of 50 millimeter (2 in) wide aluminum strips sandwiched between two rubber sheets. In order to distinguish between “legitimate” metal markings of the landmark and between arbitrary metal objects, additional parallel line segments were used (see Figure 7.8). The metal markers used as landmarks in this experiment are resilient to contamination even in harsh environments. Water, dust, and lighting condition do not affect the readability of the metal sensor [Matsuda and Yoshikawa, 1989]. Rubber sheet Figure 7.8: The Z-shaped landmark. Note the secondary lines parallel to the horizontal Z-stripes. The secondary lines help distinguish the marker from random metal parts on the road. (Courtesy of [Matsuda and Yoshikawa, 1989].)
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7KH8QLYHUVLW\RI0LFKLJDQ Where am I
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Acknowledgments This research was s
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INTRODUCTION Leonard and Durrant-Wh
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Part I Sensors for Mobile Robot Pos
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Systems-at-a-Glance Tables Landmark
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REFERENCES 1. Abidi, M. and Chandra
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238 References 31. Boltinghouse, S.
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240 References MOBOT-IV.” Proceed
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242 References 90. Dahlin, T. and K
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244 References 120. Fisher, D., Hol
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246 References 156. Janet, J., Luo,
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252 References 249. Sanders, G.A.,
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254 References 278. Turpin, D.R., 1
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256 References 309. EATON - Eaton-K
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258 References 349. SPSi - Spatial
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260 References 380. MacKenzie, P. a
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262 Index AC ..............47, 59,
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