ROBOT DYNAMIC OBSTACLE AVOIDANCE METHOD BASED ON MULTIMODAL SPIKING NEURAL NETWORK

    公开(公告)号:US20240028036A1

    公开(公告)日:2024-01-25

    申请号:US18373623

    申请日:2023-09-27

    Abstract: The present invention provides a robot dynamic obstacle avoidance method based on a multimodal spiking neural network. The present invention realizes a robot obstacle avoidance method in a dynamic environment by fusing laser radar data and processed event camera data and combining with the intrinsic learnable threshold of the spiking neural network for a scenario comprising dynamic obstacles. It solves the difficulty of failure of obstacle avoidance due to the difficulty in perceiving the dynamic obstacles in the obstacle avoidance task of a robot. The present invention helps the robot to fully perceive the static information and the dynamic information of the environment, uses the learnable threshold mechanism of the spiking neural network for efficient reinforcement learning training and decision making, and realizes autonomous navigation and obstacle avoidance in the dynamic environment. An event data enhanced model is combined to better adapt to the dynamic environment for obstacle avoidance.

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