Abstract
The problem of intelligent path planning by autonomous robotic vehicles in unordered environments is considered for the case where the two restrictions are imposed at a time: a) the world is unknown and must be modelled by the robot on the basis of sensory data; b) only local sensory information in a very limited vicinity of a current location of the robot is available. An approach to non-heuristic obstacle avoidance and path generation based upon self-learning is presented. Algorithms for active formation of world models are described that permit the robot or a team of interacting robots to fuse “local knowledge” in a growing graph model in order to plan rational (eventually optimal) paths.
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