A CNN Based Q-learning Algorithm for Path Planning of Automated Guided Vehicle

Ma, Zhuo; Wang, Dongqing

doi:10.1109/iceemt52412.2021.9601907

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Recently, Ma and Wang [124] proposed to overcome the problems of slow convergence speed, highest number of iterations and unstable convergence performance in Q-learning algorithm by improving NN algorithm for AGV path planning. Their finding was shows that the improved NN algorithm increased the convergences speed with better effect compared to traditional Qlearning algorithm.…”

Section: B Neural Networkmentioning

confidence: 99%

A Survey on Navigation Approaches for Automated Guided Vehicle Robots in Dynamic Surrounding

2023

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Automated Guided Vehicle (AGV) have received a lot of attention in recent years in terms of both hardware and software research. Nowadays, the AGV offer more adaptable and effective industrial and transportation system solutions. An AGV navigation techniques is essential to its operation. The decision to use AGV navigation is not simple, though to be suite to the application and sufficient. This paper survey the navigation approaches applied in AGV in the past five years of published academic research. In doing so, this work responds to three related questions: (1) is the AGV classical navigation techniques still relevant to the current application area; (2) is the heuristic navigation techniques itself able to optimize AGV movement in term of guide and strategy; (3) is the use of Artificial Intelligence (AI) in AGV navigation techniques able to increase system performance. As a result, numerous techniques for the AGV navigation have been developed globally. On the other hand, the most popular navigation approaches are provided below for more research.

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Section: B Neural Networkmentioning

confidence: 99%