Manufacturing automation, especially through implementation of autonomous ground vehicle (AGV) technology, has been under intensive study due to increased productivity and reduced variations. The objective of this paper is to present an algorithm on scheduling of an AGV that traverses desired locations on a manufacturing floor. Although many algorithms have been developed to achieve this objective, most of them rely on exhaustive search, which is time-consuming. A novel two-step algorithm that generates “good,” but not necessarily optimal, solutions for relatively large data sets (≈1000 points) is proposed, taking into account time constraints. A tradeoff analysis of computational expense versus algorithm performance is discussed. The algorithm enables the AGV to find a tour, which is as good as possible within the time constraint, using which it can travel through all given coordinates before returning to the starting location or a specified end point. Compared to exhaustive search methods, this algorithm generates results within a stipulated computation time of 30 s on a laptop personal computer.

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