ProNav: 脚式ロボットの屋外ナビゲーションのための固有受容性感覚に基づく走破性推定
ProNav: Proprioceptive Traversability Estimation for Legged Robot Navigation in Outdoor Environments
脚式ロボットの関節エンコーダ・力・電流センサのみから地形の安定性や嵌まり込みリスクを推定し、歩容を最適化して屋外ナビゲーションの成功率と省エネ性を向上させる手法を提案。
著者: Mohamed Elnoor, Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Dinesh Manocha
分類: cs.RO
原文アブストラクト
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.
