Intelligent robots are increasingly being deployed across industries ranging from manufacturing to household applications and outdoor exploration. Their autonomous obstacle avoidance capabilities in complex environments have become a critical factor determining operational stability. Multimodal perception technology, which integrates visual, auditory, tactile, and LiDAR data, provides robots with comprehensive environmental awareness. By establishing efficient autonomous obstacle avoidance decision-making mechanisms based on this information, the system’s adaptability to challenging scenarios can be significantly enhanced. This study investigates the integration of multimodal perception with autonomous obstacle avoidance decision-making, analyzing the acquisition and processing of perceptual information, core modules and logic of decision-making mechanisms, and proposing optimization strategies for specific scenarios. The research aims to provide theoretical references for advancing autonomous obstacle avoidance technology in intelligent robots, enabling safer and more flexible movement in diverse environments.