Path Planning of Indoor Mobile Educational Robot Based on Improved Deep Reinforcement Learning
Date
2023
ISBN
Journal Title
Journal ISSN
Volume Title
Resource Type
Conference paper
Publisher
Springer Science and Business Media Deutschland GmbH
Journal Title
Path Planning of Indoor Mobile Educational Robot Based on Improved Deep Reinforcement Learning
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Abstract
With the maturity of artificial intelligence and Internet of Things technology, the research on robots has also become one of the hotspots of artificial intelligence. Indoor mobile educational robots are an important part of machine intelligence. Research on the path of indoor mobile educational robots has become a key point in machine research. The purpose of this paper is to study the path planning of indoor mobile educational robots to improve deep reinforcement learning. This article first summarizes the research status of mobile educational robots at home and abroad. On this basis, the kinematics model of the indoor mobile educational robot is researched and analyzed. This article systematically elaborates the path planning based on the Actor-Critic algorithm and the deep reinforcement learning training model based on the minimum depth of field information. And use comparative analysis method, observation method and other research methods to carry out experimental research on the theme of this article. Research shows that the Actor-Critic algorithm proposed in this paper is shorter in path planning time and path distance than traditional algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Citation
Lecture Notes on Data Engineering and Communications Technologies