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

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。

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.