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.

翻译：摘要：我们提出了一种名为ProNav的新方法，该方法利用本体感知信号对具有挑战性的户外地形进行可穿越性估计，以实现足式机器人的自主导航。我们的方法通过足式机器人的关节编码器、力传感器和电流传感器分别获取关节位置、受力和电流消耗数据，以准确评估地形的稳定性、对机器人运动的阻力、卡陷风险及碰撞风险。基于这些因素，我们计算机器人的最佳步态以最大化稳定性，从而降低能量消耗。该方法还能预测在复杂地形中的潜在碰撞，并执行预判性规避行为。我们将ProNav与基于外感受的方法集成，以在布满茂密植被、高粒度地形、负障碍物等真实环境中实现导航。与仅依赖外感受的方法相比，我们的方法在成功率上提升高达40%，能耗降低15.1%。