Abstract
Autonomous moving systems, such as free moving robots and ‘nursing’ robots, accumulate errors during path tracking. While moving, correlation between measurement of the pattern characterizing the path and a previously recorded pattern stored in the computer of the tracking system can be found. Based on this correlation, it is possible to evaluate the system deviations from the desired path and calculate the required action to correct the movement of the vehicle.
For certain applications the path characterizing pattern can be based on piecewice sections of nonparallel straight lines. The pattern can be obtained by filtering the straight lines from a picture of the path vicinity. To perform the required correlation between the actual measurement and this pattern a novel correlation algorithm, based on MSD (Mean Square Difference), is derived. MSD has an advantage over other methods for path pattern correlation, when there are no scaling errors between the measurement and the pattern in memory. Moreover, MSD converges to ML (Maximum Likelihood) for errors which are small with respect to the pattern measurement.
Two main issues in implementing the new correlation process are discussed:
-
(A)
The near optimum performance of the new correlation algorithm, along with its simplicity, and the way in which it ‘handles’ real-time calculations with minimum computational effort.
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(B)
The evaluation of the performance bound for the correlation process.
It is shown that, with the proposed method, deterministic errors in measuring the distances to these lines do not affect the correlation in the longitudinal (x) direction. Thus, only random measurement errors among the lines will cause correlation errors in x. In the lateral (y) direction, however, the obtained error is identical in size to the bias error.
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Berman, A., Dayan, J. Piecewise straight-line correlation algorithm for navigation of autonomous systems with robotics applications. J Intell Robot Syst 10, 301–321 (1994). https://doi.org/10.1007/BF01258263
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DOI: https://doi.org/10.1007/BF01258263