Where am I? Sensors and Methods for Mobile Robot Positioning

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176 Part II Systems and Methods for Mobile Robot Positioning Fukui [1981] used a diamond-shaped landmark and applied a least-squares method to find line segments in the image plane. Borenstein [1987] used a black rectangle with four white dots in the corners. Kabuka and Arenas [1987] used a half-white and half-black circle with a unique bar-code for each landmark. Magee and Aggarwal [1984] used a sphere with horizontal and vertical calibration circles to achieve three-dimensional localization from a single image. Other systems use reflective material patterns and strobed light to ease the segmentation and parameter extraction [Lapin, 1992; Mesaki and Masuda, 1992]. There are also systems that use active (i.e., LED) patterns to achieve the same effect [Fleury and Baron, 1992]. The accuracy achieved by the above methods depends on the accuracy with which the geometric parameters of the landmark images are extracted from the image plane, which in turn depends on the relative position and angle between the robot and the landmark. In general, the accuracy decreases with the increase in relative distance. Normally there is a range of relative angles in which good accuracy can be achieved, while accuracy drops significantly once the relative angle moves out of the “good” region. There is also a variety of landmarks used in conjunction with non-vision sensors. Most often used are bar-coded reflectors for laser scanners. For example, currently ongoing work by Everett on the Mobile Detection Assessment and Response System (MDARS) [DeCorte, 1994] uses retro-reflectors, and so does the commercially available system from Caterpillar on their Self-Guided Vehicle [Gould, 1990]. The shape of these landmarks is usually unimportant. By contrast, a unique approach taken by Feng et al. [1992] used a circular landmark and applied an optical Hough transform to extract the parameters of the ellipse on the image plane in real time. 7.2.1 Global Vision Yet another approach is the so-called global vision that refers to the use of cameras placed at fixed locations in a workspace to extend the local sensing available on board each AGV [Kay and Luo, 1993]. Figure 7.4 shows a block diagram of the processing functions for vehicle control using global vision. In global vision methods, characteristic points forming a pattern on the mobile robot are identified and localized from a single view. A probabilistic method is used to select the most probable matching according to geometric characteristics of those percepts. From this reduced search space a prediction-verification loop is applied to identify and to localize the points of the pattern [Fleury and Baron, 1992]. One advantage of this approach is that it allows the operator to monitor robot operation at the same time. 7.3 Artificial Landmark Navigation Systems Many systems use retroreflective barcodes as artificial landmarks, similar to the ones used in beacon navigation systems. However, in this book we distinguish between retroreflective bar-codes used as artificial landmarks and retroreflective poles used as “beacons.” The reason is that if retroreflective markers (with or without bar-code) are attached to the walls of a room and their function is merely to aid in determining the location of the wall, then these markers do not
Chapter 7: Landmark Navigation 177 Off-line central processing Load database Transport equip. DB Camera placement 3-D world model Fixed obj. DB Create 3-D model Facility layout On-line central processing System status Mobile object tracking 2-D world model Free path verification Collision avoidance Path planning Dispatch. request Camera FM radio Transmit/ receive Infrared AGV controller Controls Vehicle status Vehicle processing Transmit/ receive Ultrasonic sensors Infrared sensors Odometers Bumpers kay_luo.ds4, .wmf, 11/12/94 Figure 7.4: Block diagram of the processing functions for vehicle control using global vision. (Adapted from [Kay and Luo, 1993].) function as beacons. By contrast, if markers are used on arbitrarily placed poles (even if the location of these poles is carefully surveyed), then they act as beacons. A related distinction is the method used for computing the vehicle's position: if triangulation is used, then the reflectors act as beacons. 7.3.1 MDARS Lateral-Post Sensor Currently ongoing work by Everett on the Mobile Detection Assessment and Response System (MDARS) [Everett et al., 1994; DeCorte, 1994] uses passive reflectors in conjunction with a pair of fixed-orientation sensors on board the robot. This technique, called lateral-post detection, was incorporated on MDARS to significantly reduce costs by exploiting the forward motion of the robot for scanning purposes. Short vertical strips of 2.5 centimeters (1 in) retroreflective tape are placed on various immobile objects (usually structural-support posts) on either side of a virtual path segment. The exact x-y locations of these tape markers are encoded into the virtual path program. Installation takes only seconds, and since the flat tape does not protrude into the aisle at all, there is little chance of damage from a passing fork truck. A pair of Banner Q85VR3LP retroreflective proximity sensors mounted on the turret of the Navmaster robot face outward to either side as shown in Figure 7.5 These inexpensive sensors respond to reflections from the tape markers along the edges of the route, triggering a “snapshot” virtual path instruction that records the current side-sonar range values. The longitudinal position of the platform is updated to the known marker coordinate, while lateral position is inferred from the sonar data, assuming both conditions fall within specified tolerances.
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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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264 Index Datums ..................
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