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 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 35% in terms of energy efficiency.

翻译：我们提出了一种名为ProNav的新型方法，该方法利用本体感觉信号对具有挑战性的室外地形进行可通过性估计，以支持自主腿足机器人导航。我们的方法使用来自腿足机器人关节编码器、力传感器和电流传感器的数据，分别测量关节位置、力和电流消耗，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，我们计算机器人合适的轨迹和步态，以最大化稳定性并最小化能量消耗。我们的方法还可用于预测具有挑战性地形中的即将发生的碰撞，并执行行为以主动避免它们。我们将ProNav与一种在茂密植被中导航的方法相结合，并在具有密集灌木、高颗粒度、负障碍物等现实地形中展示了该方法的效果。我们的方法在成功率方面提升了高达50%，在能源效率方面提升了高达35%。