Intelligent Navigation of Indoor Robot Based on Improved DDPG Algorithm

被引：0

作者：

He, Xuemei ^{[1
]}

Kuang, Yin ^{[1
]}

Song, Ning ^{[1
]}

Liu, Fan ^{[1
]}

机构：

[1] College of Art and Design, Shaanxi University of Science & Technology, Xi'an, China

来源：

Mathematical Problems in Engineering | 2023年 / 2023卷

关键词：

Decision making - Deep learning - Gaussian noise (electronic) - Indoor positioning systems - Intelligent robots - Learning algorithms - Motion planning - Navigation systems;

D O I：

10.1155/2023/6544029

中图分类号：

学科分类号：

摘要：

Targeting the problem of autonomous navigation of indoor robots in large-scale, complicated, and unknown environments, an autonomous online decision-making algorithm based on deep reinforcement learning is put forward in this paper. Traditional path planning methods rely on the environment modeling, which can cause more workload of calculating. In this paper, the sensors to detect surrounding obstacles are combined with the DDPG (deep deterministic policy gradient) algorithm to input environmental perception and control the action direct output, which enables robots to complete the tasks of autonomous navigation and distribution without relying on environment modeling. In addition, the algorithm preprocesses the relevant data in the learning sample with Gaussian noise, facilitating the agent to adapt to noisy training environment and improve its robustness. The simulation results show that the optimized DL-DDPG algorithm is more efficient on online decision-making for the indoor robot navigation system, which enables the robot to complete autonomous navigation and intelligent control independently. © 2023 Xuemei He et al.

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