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 dense vegetation and demonstrate our method's benefits in real-world terrains with dense bushes, high granularity, negative obstacles, etc. Our method shows an improvement up to 50% in terms of success rate and up to 22.5% reduction in terms of energy consumption compared to exteroceptive-based methods.

翻译：摘要：本文提出了一种新颖方法——ProNav，该方法利用本体感觉信号对户外复杂地形进行可通行性估计，以实现腿式机器人的自主导航。我们的方法通过采集腿式机器人关节编码器、力传感器和电流传感器的数据，分别测量关节位置、受力及电流消耗，从而准确评估地形的稳定性、对机器人运动的阻力、陷入风险及碰撞风险。基于这些因素，我们计算机器人的最优步态以最大化稳定性，进而降低能耗。该方法还能预测复杂地形中即将发生的碰撞，并执行行为提前规避。我们将ProNav与基于外部感知的方法相结合，用于导航茂密植被区域，并在包含密集灌木、高颗粒度地形、负障碍物等真实场景中验证了方法优势。与纯外部感知方法相比，我们的方法在成功率上提升高达50%，能耗降低22.5%。