Simple obstacle detection to prevent miscalculation of line location and orientation in lie following using statistically calculated expected values

摘要

Visual line following in mobile robotics can be made more complex when objects are placed on or around the line being followed. An algorithm is presented that suggests a manner in which a good line track can be discriminated from a bad line track using the expected size of the line. The mobile robot in this case can determine the size of the width of the line. It calculates a mean size for the line as it moves and maintains a set size of samples, which enable it to adapt to changing conditions. If a measurement is taken that falls outside of what is to be expected by the robot, then it treats the measurement as undependable and as such can take measures to deal with what it believes to be erroneous data. Techniques for dealing with erroneous data include attempting to look around the obstacle or making an educated guess as to where the line should be. The system discussed has the advantage of not needing to add any extra equipment to discover if an obstacle is corrupting its measurements. Instead, the robot is able to determine if data is good or bad based upon what it expects to find.