Wang Ruilong, Wang Ming, Zhao Qianchuan, Gong Yanling, Zuo Lingchen, Zheng Xuehan, Gao He
School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China.
Department of Automation, Tsinghua University, Beijing 100084, China.
Biomimetics (Basel). 2024 Apr 8;9(4):221. doi: 10.3390/biomimetics9040221.
Precision control of multiple robotic fish visual navigation in complex underwater environments has long been a challenging issue in the field of underwater robotics. To address this problem, this paper proposes a multi-robot fish obstacle traversal technique based on the combination of cross-modal variational autoencoder (CM-VAE) and imitation learning. Firstly, the overall framework of the robotic fish control system is introduced, where the first-person view of the robotic fish is encoded into a low-dimensional latent space using CM-VAE, and then different latent features in the space are mapped to the velocity commands of the robotic fish through imitation learning. Finally, to validate the effectiveness of the proposed method, experiments are conducted on linear, S-shaped, and circular gate frame trajectories with both single and multiple robotic fish. Analysis reveals that the visual navigation method proposed in this paper can stably traverse various types of gate frame trajectories. Compared to end-to-end learning and purely unsupervised image reconstruction, the proposed control strategy demonstrates superior performance, offering a new solution for the intelligent navigation of robotic fish in complex environments.
在复杂水下环境中对多机器人鱼视觉导航进行精确控制长期以来一直是水下机器人领域的一个具有挑战性的问题。为了解决这个问题，本文提出了一种基于跨模态变分自编码器（CM-VAE）和模仿学习相结合的多机器人鱼障碍物穿越技术。首先，介绍了机器人鱼控制系统的总体框架，其中利用CM-VAE将机器人鱼的第一人称视角编码到低维潜在空间中，然后通过模仿学习将该空间中的不同潜在特征映射到机器人鱼的速度指令上。最后，为了验证所提方法的有效性，在单条和多条机器人鱼的线性、S形和圆形门框轨迹上进行了实验。分析表明，本文提出的视觉导航方法能够稳定地穿越各种类型的门框轨迹。与端到端学习和纯无监督图像重建相比，所提控制策略表现出优越的性能，为复杂环境中机器人鱼的智能导航提供了一种新的解决方案。
