行動認識と拡張階層型二次計画法による親和的で人間工学的な人間-ロボット協働
Sociable and Ergonomic Human-Robot Collaboration through Action Recognition and Augmented Hierarchical Quadratic Programming
人間の行動認識と意図予測を視覚技術で行い、拡張階層型二次計画法に統合することで、ロボットの反応行動と人間の人間工学を階層的に最適化する協働フレームワークを提案した。
著者: Francesco Tassi, Francesco Iodice, Elena De Momi, Arash Ajoudani
分類: cs.RO
原文アブストラクト
The recognition of actions performed by humans and the anticipation of their intentions are important enablers to yield sociable and successful collaboration in human-robot teams. Meanwhile, robots should have the capacity to deal with multiple objectives and constraints, arising from the collaborative task or the human. In this regard, we propose vision techniques to perform human action recognition and image classification, which are integrated into an Augmented Hierarchical Quadratic Programming (AHQP) scheme to hierarchically optimize the robot's reactive behavior and human ergonomics. The proposed framework allows one to intuitively command the robot in space while a task is being executed. The experiments confirm increased human ergonomics and usability, which are fundamental parameters for reducing musculoskeletal diseases and increasing trust in automation.
