Evolving cyclic control for a hexapod robot performing area coverage

For a robot to search an entire area, it must follow a path that allows the range of its sensors to cover all parts of the area. This problem is a subset of path planning called area coverage. Most work done in this type of path planning has concentrated on ways of dividing the area up to avoid obstacles while covering the area. This is an important step in the process, but often takes for granted the movement of the robot within clear areas. This is not a problem if the robot has sufficient calibration to ensure the accuracy of calculated turns or if it has accurate enough navigational devices to keep track of its location. However, simple legged robots usually lack both of these attributes. It is difficult to make turns that fit a specified arch and sufficient on board navigational devices are expensive and/or too large to carry. In this paper, we use cyclic genetic algorithms to learn the control cycles required to make an actual hexapod robot perform area coverage.