SwipeBot: 雑然環境における可動障害物を押しのけるDNNベース自律ナビゲーション
SwipeBot: DNN-based Autonomous Robot Navigation among Movable Obstacles in Cluttered Environments
RGB-D画像から障害物の種類をCNNで分類し、押すべき物体と力を判断して経路を確保する車輪ロボットのナビゲーション手法を提案。ROS上で実装し、従来手法では行き詰まる領域でも経路を構築できることをシミュレーションで示した。
著者: Nikolay Zherdev, Mikhail Kurenkov, Kristina Belikova, Dzmitry Tsetserukou
分類: cs.RO
原文アブストラクト
In this paper, we propose a novel approach to wheeled robot navigation through an environment with movable obstacles. A robot exploits knowledge about different obstacle classes and selects the minimally invasive action to perform to clear the path. We trained a convolutional neural network (CNN), so the robot can classify an RGB-D image and decide whether to push a blocking object and which force to apply. After known objects are segmented, they are being projected to a cost-map, and a robot calculates an optimal path to the goal. If the blocking objects are allowed to be moved, a robot drives through them while pushing them away. We implemented our algorithm in ROS, and an extensive set of simulations showed that the robot successfully overcomes the blocked regions. Our approach allows a robot to successfully build a path through regions, where it would have stuck with traditional path-planning techniques.
