面向动态避障的事件增强多模态脉冲神经网络
Event-Enhanced Multi-Modal Spiking Neural Network for Dynamic Obstacle Avoidance
AI总结：
针对现有SNN避障方法难以处理动态场景的问题，提出融合事件相机与激光数据的EEM-SAN网络，通过无监督表示学习提取运动信息、可学习阈值实现多模态融合，显著提升动态避障性能。
摘要：
自主避障对于移动机器人等智能体在环境中导航至关重要。现有最优方法采用深度强化学习（DRL）训练脉冲神经网络（SNN），以在复杂或未知场景中实现高能效与快速推理。这类方法通常假设环境为静态，但真实场景中的障碍物往往是动态的。障碍物的运动提升了环境复杂度，对现有方法构成了巨大挑战。本研究从两方面实现鲁棒的动态避障：其一，引入神经形态视觉传感器（即事件相机），提供与传统激光深度数据互补的运动线索，以应对动态障碍物；其二，提出基于DRL的事件增强多模态脉冲执行器网络（EEM-SAN），该网络通过无监督表示学习从运动事件数据中提取信息，并通过可学习阈值融合激光与事件相机数据。实验表明，EEM-SAN的性能显著优于现有最优避障方法，尤其在动态避障任务中优势突出。
英文摘要：
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.
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