  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.
