<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, a new intelligent robot path recognition system LADDPG, is constructed by extending the DDPG algorithm with the support of the deep learning network algorithm. The path recognition system is optimized by using the memory system and the decision module guided by the attention mechanism, which solves the problems of the existing DRL intelligent robot path recognition method that cannot store long-time memory and the training time is too long. The system in this paper is validated by conducting path recognition experiments under different road conditions and conducting dynamic obstacle avoidance test experiments. The system can maintain excellent noise immunity and a short path recognition time of 51 ms under the conditions of missing road conditions, circuitous road conditions and large curvature, and in the presence of multiple dynamic obstacles, the system can maintain a collision rate close to 0 while maintaining a very high success rate of 0.98, and the required path recognition time is much lower than that of other methods, with an average reward value of 0.4151. This paper’s system is highly accurate in recognizing intelligent robot paths and has high application value and capability.</p>
</abstract>

In this paper, a new intelligent robot path recognition system LADDPG, is constructed by extending the DDPG algorithm with the support of the deep learning network algorithm. The path recognition system is optimized by using the memory system and the decision module guided by the attention mechanism, which solves the problems of the existing DRL intelligent robot path recognition method that cannot store long-time memory and the training time is too long. The system in this paper is validated by conducting path recognition experiments under different road conditions and conducting dynamic obstacle avoidance test experiments. The system can maintain excellent noise immunity and a short path recognition time of 51 ms under the conditions of missing road conditions, circuitous road conditions and large curvature, and in the presence of multiple dynamic obstacles, the system can maintain a collision rate close to 0 while maintaining a very high success rate of 0.98, and the required path recognition time is much lower than that of other methods, with an average reward value of 0.4151. This paper’s system is highly accurate in recognizing intelligent robot paths and has high application value and capability.

Research on the Construction of Intelligent Robot Path Recognition System Supported by Deep Learning Network Algorithm

Gunma University, Electronic and Information Engineering·Mathematical Sciences

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, a new intelligent robot path recognition system LADDPG, is constructed by extending the DDPG algorithm with the support of the deep learning...

Research on the Construction of Intelligent Robot Path Recognition System Supported by Deep Learning Network Algorithm

Data publikacji: 31 sty 2024

Zakres stron: -

Otrzymano: 15 gru 2023

Przyjęty: 25 gru 2023

DOI: https://doi.org/10.2478/amns-2024-0098

Słowa kluczowe
Deep learning, Intelligent robot, Path recognition system, LSTM, LADDPG

© 2024 Kan Ni et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the Construction of Intelligent Robot Path Recognition System Supported by Deep Learning Network Algorithm

Data publikacji: 31 sty 2024

Zakres stron: -

Otrzymano: 15 gru 2023

Przyjęty: 25 gru 2023

DOI: https://doi.org/10.2478/amns-2024-0098

Słowa kluczoweDeep learning, Intelligent robot, Path recognition system, LSTM, LADDPG

© 2024 Kan Ni et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Deep learning, Intelligent robot, Path recognition system, LSTM, LADDPG