Citation

Jian-chuan D, Jin-tong X, Ke-xin Z, Dong-qing JIA, Bing-de CUI, Xin Y. J. Graphics 2023; 44(6): 1121-1129.

Copyright

(Copyright © 2023, China Association for Science and Technology)

DOI

10.11996/JG.j.2095-302X.2023061121

PMID

unavailable

Abstract

Spiking neural network (SNN) have been widely applied in the field of mobile robot navigation and obstacle avoidance due to their low power consumption and temporal processing capabilities. However, existing SNN models are relatively simple and struggle with addressing obstacle avoidance in complex scenarios, such as dynamic obstacles with varying speeds and environmental noise interference. To tackle these challenges, a complex scene navigation and obstacle avoidance algorithm was proposed based on SNNs. This algorithm employed attention mechanisms to enhance obstacle avoidance capabilities for dynamic obstacles, enabling the model to make more accurate obstacle avoidance decisions by focusing more on the information of dynamic obstacles. Additionally, a dynamic spiking threshold was designed based on biological inspiration, allowing the model to adaptively adjust the firing of spiking signals to adapt to environments with noise interference. Experimental results demonstrated that the proposed algorithm exhibited optimal navigation and obstacle avoidance performance within virtual complex scenes. Across the three designed complex scenes (variable-speed dynamic scenes, input interference, and weight interference), the navigation obstacle avoidance success rates could reach 86.5%, 79.0%, and 76.2%, respectively. This research provided a new approach and method for solving the problem of robot navigation and obstacle avoidance in complex scenarios.

Key words: spiking neural network, navigation and obstacle avoidance, mobile robot, dynamic spiking threshold, attention mechanism

Language: en