基于脉冲神经网络的复杂场景导航避障算法

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

图学学报 ›› 2023, Vol. 44 ›› Issue (6): 1121-1129.DOI: 10.11996/JG.j.2095-302X.2023061121

• 图像处理与计算机视觉 • 上一篇下一篇

基于脉冲神经网络的复杂场景导航避障算法

丁建川¹^,²(), 肖金桐³, 赵可新¹, 贾冬青¹, 崔炳德¹, 杨鑫²()

1.河北水利电力学院计算机系，河北沧州 061016
2.大连理工大学计算机学院，辽宁大连 116024
3.河北水利电力学院基础部，河北沧州 061016

收稿日期:2023-06-27 接受日期:2023-09-13 出版日期:2023-12-31 发布日期:2023-12-17
通讯作者: 杨鑫(1984-)，男，教授，博士。主要研究方向为计算机图形学与视觉、机器人技术等。E-mail：xinyang@dlut.edu.cn
作者简介:
丁建川(1997-)，男，硕士研究生。主要研究方向为计算机视觉与机器人技术。E-mail：djc.dlut@gmail.com
基金资助:
河北水利电力学院基本科研业务费项目(SYKY2310);国家重点研发计划项目(2022ZD0210500);国家自然科学基金项目(61972067/ U21A20491);大连市杰出青年基金项目(2022RJ01)

Spiking neural network-based navigation and obstacle avoidance algorithm for complex scenes

DING Jian-chuan¹^,²(), XIAO Jin-tong³, ZHAO Ke-xin¹, JIA Dong-qing¹, CUI Bing-de¹, YANG Xin²()

1. Computer Department, Hebei University of Water Resources and Electric Engineering, Cangzhou Hebei 061016, China
2. School of Computer Science, Dalian University of Technology, Dalian Liaoning 116024, China
3. Basic Department, Hebei University of Water Resources and Electric Engineering, Cangzhou Hebei 061016, China

Received:2023-06-27 Accepted:2023-09-13 Online:2023-12-31 Published:2023-12-17
Contact: YANG Xin (1984-), professor, Ph.D. His main research interests cover computer graphics and vision, robotics technology, etc. E-mail：xinyang@dlut.edu.cn
About author:
DING Jian-chuan (1997-), master student. His main research interests cover computer vision and robotics technology.
E-mail：djc.dlut@gmail.com
Supported by:
Basic Research Business Fee Project of Hebei University of Water Resources and Electric Engineering(SYKY2310);National Key Research and Development Program of China(2022ZD0210500);National Natural Science Foundation of China(61972067/ U21A20491);Distinguished Young Scholars Funding of Dalian(2022RJ01)

摘要/Abstract

摘要：

脉冲神经网络(SNN)因其低能耗和时序性，已在移动机器人的导航和避障领域得到广泛应用。然而，现有脉冲模型相对简单，难以应对复杂场景下的避障问题，如动态变速障碍物和环境噪声干扰等。因此，提出了一种基于脉冲神经网络的复杂场景导航避障算法。该算法采用了注意力机制以增强对动态障碍物的避障能力，使得模型能够更加集中地关注动态障碍物的信息，从而更准确地做出避障决策。此外，还根据生物启发设计了一种动态脉冲阈值，使得模型可以自适应地调整脉冲信号的触发，从而适应具有噪声干扰的环境。实验结果表明，在虚拟复杂场景下，该算法表现出最优的导航避障性能，在所设计的3种复杂场景下(变速动态场景、输入干扰、权重干扰)导航避障成功率分别为86.5%，79.0%和76.2%。该研究成果为解决复杂场景下机器人导航避障问题提供了一种新的思路和方法。

关键词: 脉冲神经网络, 导航避障, 移动机器人, 动态脉冲阈值, 注意力机制

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

中图分类号:

TP391

丁建川, 肖金桐, 赵可新, 贾冬青, 崔炳德, 杨鑫. 基于脉冲神经网络的复杂场景导航避障算法[J]. 图学学报, 2023, 44(6): 1121-1129.

DING Jian-chuan, XIAO Jin-tong, ZHAO Ke-xin, JIA Dong-qing, CUI Bing-de, YANG Xin. Spiking neural network-based navigation and obstacle avoidance algorithm for complex scenes[J]. Journal of Graphics, 2023, 44(6): 1121-1129.

图/表 10

图1 复杂的导航避障场景

Fig. 1 Complex navigation and obstacle avoidance scenarios

图2 伪梯度函数拟合((a)冲激函数；(b)伪梯度函数)

Fig. 2 Fitting of pseudo-gradient functions ((a) Impulse function; (b) Pseudo-gradient function)

图3 复杂场景导航避障算法流程

Fig. 3 Algorithm flow for complex scene navigation and obstacle avoidance

图4 脉冲注意力模块结构

Fig. 4 Structure of the spiking attention module

图5 脉冲触发阈值((a)静态脉冲阈值；(b)动态脉冲阈值)

Fig. 5 Spiking firing threshold ((a) Static spiking threshold; (b) Dynamic spiking threshold)

图6 动态脉冲触发阈值模型

Fig. 6 Dynamic spiking firing threshold model

图7 Gazebo场景((a)训练场景；(b)测试场景)

Fig. 7 Gazebo scenes ((a) Training scene; (b) Testing scene)

表1 复杂场景下不同模型的导航避障成功率(%)

Table 1 Navigation obstacle avoidance success rate of different models in complex scenarios (%)

模型	变速动态场景	输入干扰	权重干扰
ROS^[21]	62.0	51.5	-
SAN^[9]	79.5	70.0	52.6
BDETT^[16]	84.0	78.4	77.0
Ours	86.5	79.0	76.2

图8 避障测试热力图

Fig. 8 Heatmap of obstacle avoidance test

表2 复杂场景下本文算法的消融实验(%)

Table 2 The ablation experiment of our algorithm in complex scenarios (%)

模型			变速动态场景	输入干扰	权重干扰
SAN^[9]	SAM	DTH	变速动态场景	输入干扰	权重干扰
√	-	-	79.5	70.0	51.6
√	√	-	85.5	72.5	51.3
√	-	√	83.0	79.0	76.0
√	√	√	86.5	79.0	76.2

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基于脉冲神经网络的复杂场景导航避障算法

Spiking neural network-based navigation and obstacle avoidance algorithm for complex scenes

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