  We propose a novel method, ProNav, which uses proprioceptive signals for
traversability estimation in challenging outdoor terrains for autonomous legged
robot navigation. Our approach uses sensor data from a legged robot's joint
encoders, force, and current sensors to measure the joint positions, forces,
and current consumption respectively to accurately assess a terrain's
stability, resistance to the robot's motion, risk of entrapment, and crash.
Based on these factors, we compute the appropriate robot gait to maximize
stability, which leads to reduced energy consumption. Our approach can also be
used to predict imminent crashes in challenging terrains and execute behaviors
to preemptively avoid them. We integrate ProNav with an exteroceptive-based
method to navigate real-world environments with dense vegetation, high
granularity, negative obstacles, etc. Our method shows an improvement up to 40%
in terms of success rate and up to 15.1% reduction in terms of energy
consumption compared to exteroceptive-based methods.
