Intelligent Terrain Adaptation Techniques for Mobile Robotic Platforms

Abstract

Autonomous mobile robotic platforms operating in unstructured environments face significant challenges due to unpredictable terrain topologies, varying soil properties, and geometric obstacles. Traditional reactive motion planners often fail or suffer severe efficiency losses when transitioning between highly distinct surfaces such as sand, gravel, mud, and solid rock. This paper introduces a comprehensive framework for intelligent terrain adaptation that combines exteroceptive perception and proprioceptive feedback to dynamically optimize robotic locomotion parameters. By leveraging deep learning-based visual-tactile classification alongside a real-time predictive control system, the proposed mobile robotic architecture achieves autonomous adaptation of wheel torque, suspension geometry, and path selection. Experimental validations conducted across four distinct simulated and real-world testing grounds demonstrate substantial improvements in energy efficiency, slip reduction, and stability control compared to traditional static locomotion algorithms. The results show that multi-modal sensor fusion provides the reliable situational awareness necessary for long-term robot autonomy in search-and-rescue, planetary exploration, and agricultural operations.