離散地形の動的歩行のための深層視覚認識
Deep Visual Perception for Dynamic Walking on Discrete Terrain
足場が離散的に配置された地形を二足歩行ロボットが動的に歩くため、着地直前の画像から次の歩幅を推定する畳み込みニューラルネットワークを設計し、視覚フィードバック制御で100歩以上の連続歩行を実現した。
著者: Avinash Siravuru, Allan Wang, Quan Nguyen, Koushil Sreenath
分類: cs.RO
原文アブストラクト
Dynamic bipedal walking on discrete terrain, like stepping stones, is a challenging problem requiring feedback controllers to enforce safety-critical constraints. To enforce such constraints in real-world experiments, fast and accurate perception for foothold detection and estimation is needed. In this work, a deep visual perception model is designed to accurately estimate step length of the next step, which serves as input to the feedback controller to enable vision-in-the-loop dynamic walking on discrete terrain. In particular, a custom convolutional neural network architecture is designed and trained to predict step length to the next foothold using a sampled image preview of the upcoming terrain at foot impact. The visual input is offered only at the beginning of each step and is shown to be sufficient for the job of dynamically stepping onto discrete footholds. Through extensive numerical studies, we show that the robot is able to successfully autonomously walk for over 100 steps without failure on a discrete terrain with footholds randomly positioned within a step length range of 45-85 centimeters.
