H. Suzuki and S. Arimoto, "Visual control of autonomous mobile robot based on self-organizing model for pattern learning," Journal of Robotic Systems, (5) 5:453-470, 1988.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....the instruments. 2.1 Shared Autonomy and Cooperative Control There is a large body of literature concerning provably stable control techniques for robots. Standard paradigms include 1) pre programmed trajectory control of position [13, 14] and force [15, 16] 2) fully autonomous robots (e.g. [17 19]) and 3) master slave teleoperators (e.g. 20 22] In our case, we are interested in developing provably stable controls for cases where both the robot and the human manipulate a single tool in contact with a compliant environment. The work most relevant to this includes that of Kazarooni ....

Suzuki, H. and S. Arimoto, Visual control of autonomous mobile robot based on selforganizing model for pattern learning. Journal of Robotic Systems, 1988. 5(5): p. 453-470.

....possible application of such algorithms, consider the problem of a robot attempting to match an image of its current location to an image of a known landmark taken at some time in the past. For the sake of efficiency, one can use one dimensional visual data (of a 360 ffi view) to do the matching [48, 60, 70, 77]. Standard patternmatching approaches might encounter difficulties if the robot is translated and rotated slightly from the location and orientation where the first image was taken. To apply our learning algorithms, we pre process the images, placing points where there are significant changes in ....

....real time. To reduce the processing time required by the landmark matching algorithm, some have proposed the use of imaging systems that generate a one dimensional array of light intensities taken at eye level (see e.g. Hong et al. 48] Levitt and Lawton [60] Pinette [70] and Suzuki and Arimoto [77]) We now briefly describe one such imaging system [48, 70] In their robot a spherical mirror is mounted above an upward pointing camera that enables it to instantaneously obtain a 360 ffi view of the world. See Figure 2.1 for a picture of such a robot. The view of the world obtained by this ....

H. Suzuki and S. Arimoto. Visual control of autonomous mobile robot based on self-organizing model for pattern learning. Journal of Robotic Systems, 5(5):453-- 470, 1988.

....the instruments. 2.1 Shared Autonomy and Cooperative Control There is a large body of literature concerning provably stable control techniques for robots. Standard paradigms include 1) pre programmed trajectory control of position [13, 14] and force [15, 16] 2) fully autonomous robots (e.g. [17 19]) and 3) masterslave teleoperators (e.g. 20 22] In our case, we are interested in developing provably stable controls for cases where both the robot and the human manipulate a single tool in contact with a compliant environment. The work most relevant to this includes that of Kazarooni ....

Suzuki, H. and S. Arimoto, Visual control of autonomous mobile robot based on selforganizing model for pattern learning. Journal of Robotic Systems, 1988. 5(5): p. 453-470.

....the instruments. 2.1 Shared Autonomy and Cooperative Control There is a large body of literature concerning provably stable control techniques for robots. Standard paradigms include 1) pre programmed trajectory control of position [13, 14] and force [15, 16] 2) fully autonomous robots (e.g. [17 19]) and 3) masterslave teleoperators (e.g. 20 22] In our case, we are interested in developing provably stable controls for cases where both the robot and the human manipulate a single tool in contact with a compliant environment. The work most relevant to this includes that of Kazarooni ....

Suzuki, H. and S. Arimoto, Visual control of autonomous mobile robot based on selforganizing model for pattern learning. Journal of Robotic Systems, 1988. 5(5): p. 453-470.

....be performed in realtime. To reduce the processing time required by the landmark matching algorithm, some are proposing the use of imaging systems that generate a one dimensional array of light intensities taken at eye level (see e.g. Hong et al. 1992) Levitt and Lawton (1990) Pinette (1993) Suzuki and Arimoto (1988)) We now briefly de PAC LEARNING OF ONE DIMENSIONAL PATTERNS 3 Not available electronically. Figure 1. The imaging system on the robot. This figure comes directly from Pinnette s (1993) thesis. scribe one such imaging system (Hong et al. 1992) and Pinnette (1993) In their robot a ....

Suzuki, H. & Arimoto, S. (1988). Visual control of autonomous mobile robot based on selforganizing model for pattern learning. Journal of Robotic Systems, 5(5): pp. 453-470.

.... and Scott of using learning versus pattern matching for the landmark matching problem can be applied to a wide range of data, the rest of their work was specific to the data from an imaging system that generates a one dimensional array of light intensities (called a signature) taken at eye level [17, 22, 29, 33]. The motivation for using one dimensional data is to reduce the processing time. For some settings, such as an office environment, it seems feasible that the signature taken at eye level is sufficient. On the other hand, if one wants to design a landmark matching data for a Mars rover, such an ....

H. Suzuki and S. Arimoto. Visual control of autonomous mobile robot based on selforganizing model for pattern learning. Journal of Robotic Systems, 5(5):453--470, 1988.

.... and Scott of using learning versus pattern matching for the landmark matching problem can be applied to a wide range of data, the rest of their work was specific to the data from an imaging system that generates a one dimensional array of light intensities (called a signature) taken at eye level [11, 14, 19, 20]. The motivation for using one dimensional data is to reduce the processing time. For some settings, such as an office environment, it seems feasible that the signature taken at eye level is sufficient. On the other hand, if one wants to design a landmark matching data for a Mars rover, such an ....

H. Suzuki and S. Arimoto. Visual control of autonomous mobile robot based on self-organizing model for pattern learning. Journal of Robotic Systems, 5(5):453--470, 1988.

....be performed in real time. To reduce the processing time required by the landmark matching algorithm, some have proposed the use of imaging systems that generate a onedimensional array of light intensities taken at eye level (see e.g. Hong et al. 1992) Levitt and Lawton (1990) Pinette (1993) Suzuki and Arimoto (1988)) We now briefly describe one such imaging system (Hong et al. 1992) and Pinette (1993) A NOISE TOLERANT PATTERN LEARNING ALGORITHM 3 Not available electronically. Figure 1. The imaging system on the robot. This figure comes directly from Pinette s (1993) thesis. In their robot a spherical ....

Suzuki, H. & Arimoto, S. (1988). Visual control of autonomous mobile robot based on selforganizing model for pattern learning. Journal of Robotic Systems, 5(5), 453--470.

....that the landmark matching algorithm can be performed in realtime. To reduce the processing time required by the landmark matching algorithm, some are proposing the use of imaging systems that generate a one dimensional array of light intensities taken at eye level [HTP 92, LL90, Pin93, SA88] We now briefly describe one such imaging system (see Hong et al. HTP 92] and Pinnette [Pin93] In their robot a spherical mirror is mounted above an upward pointing camera on a robot thus enabling it to instantaneously obtain a 360 degree view of the world. See Figure 2 for a picture of ....

Hisashi Suzuki and Suguru Arimoto. Visual control of autonomous mobile robot based on self-organizing model for pattern learning. Journal of Robotic Systems, 5(5):453--470, 1988.

....be noise tolerant. To reduce the processing time required by the landmark matching algorithm, some researchers are using an imaging system that generates a one dimensional array of light intensities (called a signature) taken at eye level (Hong et al. 1992, Levitt and Lawton 1990, Pinette 1993, Suzuki and Arimoto 1988). Most work on designing landmark matching algorithms uses a pattern matching approach to match the current signature to the signature taken at landmark position L. The matching algorithm must determine if the robot is in the near L (i.e. in a small circle centered around L) Because the visual ....

H. Suzuki & S. Arimoto. Visual control of autonomous mobile robot based on self-organizing model for pattern learning. J. of Robotic Systems, 5(5):453--470, 1988.

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H. Suzuki and S. Arimoto, "Visual control of autonomous mobile robot based on self-organizing model for pattern learning," Journal of Robotic Systems, (5) 5:453-470, 1988.

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H. Suzuki and S. Arimoto. Visual control of autonomous mobile robot based on self-organizing model for pattern learning. Journal of Robotic Systems, 5(5):453-- 470, 1988.

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