Perception maps for the local navigation of a mobile robot: a neural network approach

Sensorial perception is a key issue for the problem of robot local navigation, that is, the immediate or short-range motion planning, reacting only to the free space around the robot, without requiring a pre-defined trajectory plan. Therefore, local navigation requires no environment model and relies entirely on sensorial data. Commonly used sensors such as ultrasonic ranging devices, are known for their associated problems: specular reflections and crosstalk, essentially. However if sensors are used in an appropriate number and geometric lay-outs, the resulting spatial redundancy offers the possibility of overcoming some of those problems. This paper deals with these problems by means of special perception maps using ultrasound data. A generalised grid serves as the base of maps, and its cells have simply binary values: free or occupied. The relation between the topology of the perception map and the environment is a determinant factor for accurate reasoning. A 3-layer feed-forward neural network is used to perform the mapping between sensorial scans and grid occupancy. It was verified that the neural network handles most of the situations of specular reflections, and gives good perception maps for mid-range distances. Changes in environment, such as obstacles in vehicle´s trajectory, have also been detected, which stresses the network´s ability to generalise