視覚慣性入力とリカレントネットワークによるリスク回避型MPCのオンライン衝突回避
Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance
視覚慣性オドメトリとRNNで将来の位置不確かさを推定し、それを考慮したMPCで四足ロボットが障害物環境を安全に自律走行できる経路計画手法を提案した。
著者: Alexander Schperberg, Kenny Chen, Stephanie Tsuei, Michael Jewett, Joshua Hooks, Stefano Soatto, Ankur Mehta, Dennis Hong
分類: cs.RO, cs.CV, cs.LG, cs.SY, eess.SY
原文アブストラクト
In this paper, we propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties for safer navigation through cluttered environments. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates through each step of our MPC's finite time horizon. The RNN model is trained on a dataset that comprises of robot and landmark poses generated from camera images and inertial measurement unit (IMU) readings via a state-of-the-art visual-inertial odometry framework. To detect and extract object locations for avoidance, we use a custom-trained convolutional neural network model in conjunction with a feature extractor to retrieve 3D centroid and radii boundaries of nearby obstacles. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms, demonstrating autonomous behaviors that can plan fast and collision-free paths towards a goal point.
