A novel approach to motion modeling using fuzzy cognitive map and artificial potential fields

Artificial potential field (APF) is well established for reactive navigation of robots. Initially, this paper describes a fast and robust fuzzy-APF on an ActivMedia AmigoBot platform. Obstacle-related information is fuzzified just by sensory fusion which results in shorter runtime. The membership functions of obstacles´ range and direction have been also merged into one function for smaller block of rules. The fuzzy-APF is tested and verified virtually with non-concave obstacles. Main contribution of this article is a new approach to motion modeling. The goal is to discover decision making behaviors of the robot in wayfinding. A novel decision modeling technique is developed based on capabilities of the fuzzy cognitive map (FCM) and supervised learning using the genetic algorithm (GA). Decision productions for moving from one sub-space to another are modeled in form of decision matrices. The robot trajectory under supply of such decision matrices has likelihood of nearly 90% with its trajectory under the APF. Replication of robot motion is therefore achieved by modeling its decision behaviors in form of tangible matrices.