Autonomous Decision-Making and Control Algorithm for Live Working Robots Based on Artificial Intelligence

Authors

  • Yongling Ying National Zhejiang Cixi Power Supply Co., Ltd, Cixi 315300, Zhejiang, China
  • Sheng Zhou National Zhejiang Cixi Power Supply Co., Ltd, Cixi 315300, Zhejiang, China
  • Beishi Shen National Zhejiang Cixi Power Supply Co., Ltd, Cixi 315300, Zhejiang, China

Abstract

The current autonomous operation of live working robots in high-voltage power environments faces problems such as inadequate decision-making precision, poor adaptability, and slow response. This is mainly because the existing algorithms lack the flexibility to deal with complex dynamic environments. To solve this problem, this paper proposes an autonomous decision-making and control algorithm that combines deep learning with classical control strategies to improve the robot’s operating ability and execution efficiency in high-voltage environments. First, a Deep Convolutional Neural Network (DCNN) is used to extract the spatial features of the environment; this is used together with Long Short-Term Memory (LSTM) to model time series data to capture the dynamic change information of the environment. Then, Deep Q-Network (DQN) is applied for decision optimization,
enabling the robot to autonomously adjust its operating strategy in a complex environment. Secondly, at the control level, the robot evaluates the operating risk through a neural network, and achieves precise motion control based on PID (Proportional Integral Derivative) control and fuzzy control strategies to improve the stability and safety of the operation. The experimental results show that the task success rate of this method in the live working environment reaches 90%. When facing complex environmental changes, the execution time is reduced by about 30%, and the response time is shorter than that of the traditional algorithm. The research results verify the effectiveness of the proposed method in improving the autonomous working ability and efficiency of the live working robot.

Keywords: Autonomous Decision-making; Control Algorithm; Deep Q-Network; Reinforcement Learning; Live Working Robot

Cite As

Y. Ying, S. Zhou, B. Shen, "Autonomous Decision-Making and Control Algorithm for Live Working Robots Based on Artificial Intelligence", Engineering Intelligent Systems, vol. 34 no. 2, pp. 195-209, 2026.

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Published

2026-03-01

Issue

Vol. 34 No. 2 (2026): International Journal of Engineering Intelligent Systems

Section

Article

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