tailieunhanh - Designing Autonomous Mobile Robots phần 6

Chương 11 Việc sửa chúng tôi sẽ thực sự làm cho mỗi trục được tính toán như nó đã được cho phương vị, bằng cách nhân sửa chữa ngụ ý đầy đủ (lỗi) bởi yếu tố Q cho phù hợp với toàn bộ. xCOR = - (QFITx * Ex) Và yCOR = - (QFITy * EY) (phương trình 11,14) Giả sử rằng robot này đã bắt đầu toàn bộ quá trình này với sự không chắc chắn đáng kể cho cả hai phương vị và vị trí, | Chapter 11 The correction we will actually make for each axis is calculated as it was for azimuth by multiplying the full implied correction error by the Q factor for the whole fit. XcoR - Qfitx Ex Equation And ycoR - Qft Ey Equation Assume that this robot had begun this whole process with significant uncertainty for both azimuth and position and had suddenly started to get good images of these columns. It would not have corrected all of its axes at the same rate. At first the most improvement would be in the azimuth the most important degree of freedom then in the lateral position and then somewhat more slowly the longitudinal position. This would be because the corrections in the azimuth would reduce the azimuth uncertainty which would increase the observation quality for the position measurements. However we have only discussed how uncertainty grows not how it is reduced. Other profiles The equations discussed thus far are the most primitive possible form of fuzzy logic. In fact this is not so much a trapezoidal profile as a triangular one. Even so with the correct constants they will produce very respectable results. 164 Hard Navigation vs. Fuzzy Navigation Figure shows another possibility for this function. In this case we simply refuse to grant a quality factor greater than Q1 so the navigation will never take the whole implied correction. Furthermore any error that is greater than E2 is considered so suspect that it is not believed at all. If the breakpoints of this profile are stored as blackboard variables then the system can be trimmed during operation to product the best results. The referenced state It should be obvious that the navigation scheme we are pursuing is one that requires the robot to know roughly where it is before it can begin looking for and processing navigation features. Very few sensor systems other than GPS are global in nature and very few features are so unique as to absolutely identify the robot s position. When a