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Autonomous obstacle avoidance is of vital importance for an intelligent agentsuch as a mobile robot to navigate in its environment. Existingstate-of-the-art methods train a spiking neural network (SNN) with deepreinforcement learning (DRL) to achieve energy-efficient and fast inferencespeed in complex/unknown scenes. These methods typically assume that theenvironment is static while the obstacles in real-world scenes are oftendynamic. The movement of obstacles increases the complexity of the environmentand poses a great challenge to the existing methods. In this work, we approachrobust dynamic obstacle avoidance twofold. First, we introduce the neuromorphicvision sensor (i.e., event camera) to provide motion cues complementary to thetraditional Laser depth data for handling dynamic obstacles. Second, we developan DRL-based event-enhanced multimodal spiking actor network (EEM-SAN) thatextracts information from motion events data via unsupervised representationlearning and fuses Laser and event camera data with learnable thresholding.Experiments demonstrate that our EEM-SAN outperforms state-of-the-art obstacleavoidance methods by a significant margin, especially for dynamic obstacleavoidance.

Event-Enhanced Multi-Modal Spiking Neural Network for Dynamic Obstacle Avoidance