Utilizing Visual Information for Path Planning of Autonomous Mobile Robot

We proposed an algorithm for predicting friction coefficient from visual information for mobile robot since coefficient of friction is very important in driving on road and traversing over obstacle. Our algorithm is based on terrain classification for visual image. To predict friction coefficient from given image, we divide an image into homogeneous regions which have same material composition. The proposed method, non-contacting approach, has advantage over other methods that extract material characteristic of road by sensors contacting road surface. Obtaining information about friction coefficient before entering such terrain can be very useful for path planning and avoiding slippery areas. And we form a group of each terrain type. So, when new terrain is entered into a system, the data of new terrain are classified into each group. By grouping each terrain to use the same regression coefficients, we can reduce the amount of processing time. The proposed method will be verified by real outdoor environment with real vehicles.