Self-Adaptive Autonomous Navigation Based on Reservoir Computing in Snowy Environments

Abstract

Autonomous navigation in snowy environments is essential for snow removal robots operating in regions with heavy snowfall. However, snow accumulation obscures terrain features and introduces sensor noise, making reliable perception and navigation difficult. Moreover, snow removal robots typically operate only during winter, while the environment may change during other seasons, requiring the robot to adapt to new situations. To address these challenges, this study proposes a self-adaptive navigation framework that learns directly in real snowy environments without relying on simulation. The framework integrates reservoir computing (RC), reinforcement learning (RL), and artificial bee colony (ABC) optimization. In addition, a snow-region detection method based on thermal and grayscale images is introduced to guide the robot toward areas requiring snow removal.