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 trajectories and gait to maximize stability and minimize 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 a vision-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：我们提出了一种名为ProNav的新方法，该方法利用本体感觉信号在具有挑战性的户外地形中实现自主腿式机器人的可通行性估计。我们的方法使用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，从而精确评估地形的稳定性、对机器人运动的阻力、陷入风险以及碰撞风险。基于这些因素，我们计算机器人的合适运动轨迹和步态，以最大化稳定性并最小化能耗。我们的方法还能在复杂地形中预测即将发生的碰撞，并执行行为以提前规避。我们将ProNav与基于视觉的方法相结合，以穿越茂密植被，并在具有密集灌木丛、高粒度障碍、负障碍等现实地形中展示了该方法的效果。与基于外部感知的方法相比，我们的方法成功率提升高达50%，能耗降低达22.5%。