Perception and remembrance of the environment during real-time navigation of a mobile robot

K. Madhava Krishna    Prem K. Kalra∗   

Department of Electrical Engineering, Indian Institute of Technology, Kanpur   

This paper deals with the advantages of incorporating cognition and remembrance capabilities in a sensor-based real-time navigation algorithm. The specific features of the algorithm apart from real-time collision avoidance include spatial comprehension of the local scenario of the robot, remembrance and recollection of such comprehended scenarios and temporal correlation of similar scenarios witnessed during different instants of navigation. These features enhance the robot’s performance by providing for a memory-based reasoning whereby the robot’s forthcoming decisions are also affected by its previous experiences during the navigation apart from the current range inputs. The environment of the robot is modeled by classifying temporal sequences of spatial sensory patterns. A fuzzy classification scheme coupled to Kohonen’s self-organizing map and fuzzy ART network determines this classification. A detailed comparison of the present method with other recent approaches in the specific case of local minimum detection and avoidance is also presented. As for escaping the local minimum barrier is concerned this paper divulges a new system of rules that lead to shorter paths than the other methods. The method has been tested in concave, maze-like, unstructured and altered environments and its efficacy established