Route planning for intelligent autonomous land vehicles using hierarchical terrain representation

In this paper, an intelligent navigation system for autonomous land vehicles (ALV) using hierarchical terrain representation has been developed which can successfully negotiate an obstacle and threat-laden terrain, even if nothing is known beforehand about the terrain. The ALV stores new information in its memory as it travels, has the ability to backtrack out of unexpected dead ends, and performs spontaneous decision-making in the field based on local sensor readings. The optimal global route of the ALV journey is obtained using dynamic programming, and decision-making is accomplished via a production rule-based system. Execution examples demonstrate the power of the prototype system to solving navigation problems. This establishes the feasibility of constructing a valid ALV by combining search techniques with artificial intelligence tools such as production rule-based systems.