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The ACOS project seeks to improve and develop novel robot guidance and control systems integrating Novel Potential Field autonomous navigation techniques, multi-classifier design with direct hardware implementation. The project development brings together a number of complementary technologies to form an overall enhanced system. The work is aimed at guidance and collision avoidance control systems for applications in air, land and water based vehicles for passengers and freight. Specifically, the paper addresses the generic nature of the previously presented novel Potential Field Algorithm based on the combination of the associated rule based mathematical algorithm and the concept of potential field. The generic nature of the algorithm allows it to be efficient, not only when applied to multi-autonomous robots, but also when applied to collision avoidance between a single autonomous agent and an obstacle displaying random velocity. In addition, the mathematical complexity, which is inherent when a large number of autonomous vehicles and dynamic obstacles are present, is reduced via the incorporation of an intelligent weightless multi-classifier system which is also presented.

A NOVEL POTENTIAL FIELD ALGORITHM AND AN INTELLIGENT MULTI-CLASSIFIER FOR THE AUTOMATED CONTROL AND GUIDANCE SYSTEM (ACOS)