支援型物理インタラクション：ニューラルネットワーク検出・ナビゲーション・安全層を備えた自律飛行ロボット
Assisted Physical Interaction: Autonomous Aerial Robots with Neural Network Detection, Navigation, and Safety Layers
産業環境での安全な空中物理インタラクションのため、エッジコンピューティングを用いたニューラルネットワークによる対象検出と、制御バリア関数（CBF）に基づく安全な接近制御を組み合わせたフレームワークを提案した論文。
著者: Andrea Berra, Viswa Narayanan Sankaranarayanan, Achilleas Santi Seisa, Julien Mellet, Udayanga G. W. K. N. Gamage, Sumeet Gajanan Satpute, Fabio Ruggiero, Vincenzo Lippiello, Silvia Tolu, Matteo Fumagalli, George Nikolakopoulos, Miguel Ángel Trujillo Soto, Guillermo Heredia
分類: cs.RO, cs.CV, cs.SY, eess.SY
原文アブストラクト
The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller for safe and precise maneuvering. The target detection system is trained on a dataset under challenging visual conditions and evaluated for accuracy across various unseen data with changing lighting conditions. Depth features are utilized for target pose estimation, with the entire detection framework offloaded into low-latency edge computing. The CBF-based controller enables the UAV to converge safely to the target for precise contact. Simulated evaluations of both the controller and target detection are presented, alongside an analysis of real-world detection performance.
