自己教師あり目標指向経路計画S2P2：RGB-Dを用いた電動車椅子ナビゲーション
S2P2: Self-Supervised Goal-Directed Path Planning Using RGB-D Data for Robotic Wheelchairs
RGB-D画像と目標位置から経路ラベルを自動生成し、最適平面回帰損失で学習する自己教師あり経路計画手法を提案。地図不要で既存ナビシステムに統合でき、従来手法より高精度・高ロバスト。
著者: Hengli Wang, Yuxiang Sun, Rui Fan, Ming Liu
分類: cs.RO
原文アブストラクト
Path planning is a fundamental capability for autonomous navigation of robotic wheelchairs. With the impressive development of deep-learning technologies, imitation learning-based path planning approaches have achieved effective results in recent years. However, the disadvantages of these approaches are twofold: 1) they may need extensive time and labor to record expert demonstrations as training data; and 2) existing approaches could only receive high-level commands, such as turning left/right. These commands could be less sufficient for the navigation of mobile robots (e.g., robotic wheelchairs), which usually require exact poses of goals. We contribute a solution to this problem by proposing S2P2, a self-supervised goal-directed path planning approach. Specifically, we develop a pipeline to automatically generate planned path labels given as input RGB-D images and poses of goals. Then, we present a best-fit regression plane loss to train our data-driven path planning model based on the generated labels. Our S2P2 does not need pre-built maps, but it can be integrated into existing map-based navigation systems through our framework. Experimental results show that our S2P2 outperforms traditional path planning algorithms, and increases the robustness of existing map-based navigation systems. Our project page is available at https://sites.google.com/view/s2p2.
