Brain-Inspired Navigation Method of Multi-AUV Based on Composite Spatial Navigation Cell Model: Speed Cell and Boundary Cell
Hao Chen, Wenyu Cai, Meiyan Zhang, Xianchao Zhang · IEEE Internet of Things Journal · 2025
The brain-inspired Simultaneous Localization And Mapping technology (SLAM) in Internet of Underwater Things enables real-time location of multi-Autonomous Underwater Vehicle (AUV) with low computational overhead. However, in actual scenarios, the movement of AUV swarm interferes with visual images, which affects the performance of SLAM seriously. To deal with the above problem, a Brain-inspired Navigation Method based on Composite Spatial Navigation Cell model is proposed to achieve the autonomous navigation of AUVs, which is inspired from the speed cell and boundary cell of biological brain. Firstly, this method establishes a brain-inspired navigation scene of multi-AUV, where the stereo camera of AUVs is used to collect environmental information. Next, this method establishes speed cells based on Spiking Neural Network (SNN) to obtain semantic information, the location, score, and descriptor of feature points, which helps to estimate the motion information of AUVs such as displacement changes and heading angle changes) accurately. In addition, based on the distance information and angle information from stereo camera of AUVs to static obstacles, the proposed method calculates the activity value of boundary cells to assist in the loop-closure detection of local view cells. Finally, this method uses pose cells to represent the motion posture of AUVs, and applies experience map to record the movement trajectory of AUVs. To measure the performance of proposed method, this paper establishes an underwater SLAM dataset and a land SLAM dataset respectively, which are affected by dynamic entities. Extensive experimental results show that this method has good adaptability in different SLAM datasets, and is better than other methods in terms of trajectory error.