DSQN: Robust path planning of mobile robot based on deep spiking Q-network

Abstract

With the rapid advancement of science and technology, the field of mobile robot applications continues to expand, with path planning emerging as a fundamental yet challenging task. While traditional path planning techniques have developed into a relatively complete theoretical system, their performance in uncertain environments remains a critical area of research. To address this, we propose a novel Deep Spiking Q-Network (DSQN) algorithm that significantly enhances path planning performance by leveraging the unique advantages of spiking neural networks (SNNs). Unlike classic Q-learning and its contemporary variants, the DSQN algorithm integrates global and local information simultaneously, resulting in superior overall performance. As the third generation of neural networks, SNNs offer unparalleled robustness and energy efficiency by mimicking biological neural systems. By introducing spiking neurons into the conventional Deep Q-learning (DQN) framework, the DSQN algorithm overcomes key challenges in deep reinforcement learning (DRL), such as limited robustness and high energy consumption. The DSQN training process incorporates both surrogate gradient learning (SGL) and ANN-to-SNN conversion techniques, with SGL demonstrating remarkable effectiveness in mobile robot path planning tasks. Experimental results validate the practicality and efficiency of DSQN, showcasing improved performance across diverse test scenarios compared to the original DQN algorithm. These findings highlight the potential of DSQN to advance path planning in complex and uncertain environments, establishing it as a robust and energy-efficient solution for mobile robotics.