  Autonomous navigation in extreme mountainous terrains poses challenges due to
the presence of mobility-stressing elements and undulating surfaces, making it
particularly difficult compared to conventional off-road driving scenarios. In
such environments, estimating traversability solely based on exteroceptive
sensors often leads to the inability to reach the goal due to a high prevalence
of non-traversable areas. In this paper, we consider traversability as a
relative value that integrates the robot's internal state, such as speed and
torque to exhibit resilient behavior to reach its goal successfully. We
separate traversability into apparent traversability and relative
traversability, then incorporate these distinctions in the optimization process
of sampling-based planning and motion predictive control. Our method enables
the robots to execute the desired behaviors more accurately while avoiding
hazardous regions and getting stuck. Experiments conducted on simulation with
27 diverse types of mountainous terrain and real-world demonstrate the
robustness of the proposed framework, with increasingly better performance
observed in more complex environments.
