Abstract
Adaptive path planning and optimization for robots are a persistent challenge due to the lack of a cognitive map of the complex and dynamic environments. This paper presents a cognitive-map-based method for dynamic robot path planning by a formal maze model for workplace layouts representation. The maze model and global path trees provide a cognitive map for the robot to aware the environment. A theory of dynamic Paths Finding and Optimization (PFO) is developed. Powered by the PFO algorithm, a robot is able to autonomously explore the optimal path in a complex workplace by a single sight-read of its layout. The maze based PFO methodology provides robot for an efficient real time path cognition and optimization algorithm for dealing with dynamic workplaces. A set of experiments demonstrates the efficiency of the method, which extends traditional stepwise robot vision technologies to cognitive-map-driven global path optimization.
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