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Tham khảo tài liệu 'mit.press.introduction.to.autonomous.mobile.robots part 12', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 206 Chapter 5 Figure 5.16 Example of adaptive approximate variable-cell decomposition of an environment 21 . The rectangle bounding the free space is decomposed into four identical rectangles. If the interior of a rectangle lies completely in free space or in the configuration space obstacle it is not decomposed further. Otherwise it is recursively decomposed into four rectangles until some predefined resolution is attained. The white cells lie outside the obstacles the black inside and the gray are part of both regions. of each sensor combined with the absolute position of the robot can be used directly to update the filled or empty value of each cell. In the occupancy grid each cell may have a counter whereby the value 0 indicates that the cell has not been hit by any ranging measurements and therefore it is likely free space. As the number of ranging strikes increases the cell s value is incremented and above a certain threshold the cell is deemed to be an obstacle. The values of cells are commonly discounted when a ranging strike travels through the cell striking a further cell. By also discounting the values of cells over time both hysteresis and the possibility of transient obstacles can be represented using this occupancy grid approach. Figure 5.17 depicts an occupancy grid representation in which the darkness of each cell is proportional to the value of its counter. One commercial robot that uses a standard occupancy grid for mapping and navigation is the Cye robot 163 . There remain two main disadvantages of the occupancy grid approach. First the size of the map in robot memory grows with the size of the environment and if a small cell size is used this size can quickly become untenable. This occupancy grid approach is not compatible with the closed-world assumption which enabled continuous representations to have potentially very small memory requirements in large sparse environments. In contrast the Mobile Robot Localization 207 Figure 5.17 Example of an
