Autonomous obstacle avoidance is of vital importance for an intelligent agent such as a mobile robot to navigate in its environment. Existing state-of-the-art methods train a spiking neural network (SNN) with deep reinforcement learning (DRL) to achieve energy-efficient and fast inference speed in complex/unknown scenes. These methods typically assume that the environment is static while the obstacles in real-world scenes are often dynamic. The movement of obstacles increases the complexity of the environment and poses a great challenge to the existing methods. In this work, we approach robust dynamic obstacle avoidance twofold. First, we introduce the neuromorphic vision sensor (i.e., event camera) to provide motion cues complementary to the traditional Laser depth data for handling dynamic obstacles. Second, we develop an DRL-based event-enhanced multimodal spiking actor network (EEM-SAN) that extracts information from motion events data via unsupervised representation learning and fuses Laser and event camera data with learnable thresholding. Experiments demonstrate that our EEM-SAN outperforms state-of-the-art obstacle avoidance methods by a significant margin, especially for dynamic obstacle avoidance.

翻译：自主障碍物规避对于移动机器人等智能体在环境中导航至关重要。现有最先进的方法通过深度强化学习训练脉冲神经网络，在复杂/未知场景中实现高能效和快速推理速度。这些方法通常假设环境是静态的，但现实场景中的障碍物往往是动态的。障碍物的移动增加了环境复杂性，对现有方法提出了巨大挑战。在本研究中，我们从两方面解决鲁棒动态障碍物规避问题。首先，引入神经形态视觉传感器（即事件相机），以提供与传统激光深度数据互补的运动线索，用于处理动态障碍物。其次，开发了基于深度强化学习的事件增强多模态脉冲执行网络，通过无监督表征学习从运动事件数据中提取信息，并利用可学习阈值融合激光和事件相机数据。实验表明，我们的EEM-SAN显著优于最先进的障碍物规避方法，尤其在动态障碍物规避方面表现突出。