Autonomous Visual Navigation With Head Stabilization Control for a Salamander-Like Robot

Salamander-like robots, designed to replicate the biological features and movement principles of real salamanders, offer impressive agility and adaptability for autonomous tasks, such as environmental monitoring and disaster relief. However, their application in visual navigation within real-world scenarios remains largely unexplored. In this article, we propose a novel visual navigation framework for salamander-like robots, combining head stabilization, obstacle detection, and fuzzy control into a cohesive system. The head stabilization controller ensures stable visual perception by reducing the periodic yaw motion using a Fourier series, with neural networks estimating the coefficients and maintaining roll and pitch stability by calculating leg joint adjustments based on attitude feedback. In addition, an innovative attention-based neural network is introduced to improve obstacle detection accuracy by focusing on critical visual features, and a hierarchical fuzzy control scheme is established to generate locomotion control signals for obstacle avoidance and target navigation. Extensive experiments demonstrate the effectiveness of the framework in visual stabilization, obstacle detection, and target navigation, highlighting its practical applicability for a salamander-like robot in real-world environments.