Dual-Type Discriminator Adversarial Reservoir Computing for Robust Autonomous Navigation in a Snowy Environment

In winter, snowfall has a risk of impairing the autonomous navigation capabilities of mobile robots by obscuring road lane markings and causing sensor noise. This problem complicates the development of safe and efficient snow removal robots. In our research, we propose a novel supervised machine learning method for autonomous robot navigation in snowy environments based on Dual-Type Discriminator Adversarial Reservoir Computing (DDARC) which integrates Reservoir Computing (RC) with Generative Adversarial Networks (GANs). Utilizing depth and thermal imagery as inputs, our method can generate reliable control values for the robot's movement. Experiments in simulated environments have demonstrated that our method significantly improves the autonomous navigation capabilities of mobile robots, even in substantial environmental noise from snowfall.