Solving the local minima problem for a mobile robot by classification of spatio‐temporal sensory sequences

Abstract

The local minima problem occurs when a robot navigating past obstacles towards a desired target with no priori knowledge of the environment gets trapped in a loop. This happens especially if the environment consists of concave obstacles, mazes, and the like. To come out of the loop the robot must comprehend its repeated traversal through the same environment, which involves memorizing the environment already seen. This paper proposes a new real-time collision avoidance algorithm with the local minima problem solved by classifying the environment based on the spatio-temporal sensory sequences. A double layered classification scheme is adopted. A fuzzy rule base does the spatial classification at the first level and at the second level Kohonen’s self-organizing map and a fuzzy ART network is used for temporal classification. The robot has no prior knowledge of the environment and fuzzy rules govern its obstacle repulsing and target attracting behaviors. As the robot traverses the local environment is modeled and stored in the form of neurons whose weights represent the spatio-temporal sequence of sensor readings. A repetition of a similar environment is mapped to the same neuron in the network and this principle is exploited to identify a local minima situation. Suitable steps are taken to pull the robot out of the local minima. The method has been tested on various complex environments with obstacle loops and mazes, and its efficacy has been established. 2000 John Wiley & Sons, Inc.