知覚から意思決定へ：自律移動ロボットのためのデータ駆動型エンドツーエンド経路計画
From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robots
2Dレーザースキャンと目標位置から操舵指令を直接学習するエンドツーエンドのナビゲーションモデルを提案し、シミュレーションで訓練したモデルを実環境の未知環境にも転移できることを示した。
著者: Mark Pfeiffer, Michael Schaeuble, Juan Nieto, Roland Siegwart, Cesar Cadena
分類: cs.RO
原文アブストラクト
Learning from demonstration for motion planning is an ongoing research topic. In this paper we present a model that is able to learn the complex mapping from raw 2D-laser range findings and a target position to the required steering commands for the robot. To our best knowledge, this work presents the first approach that learns a target-oriented end-to-end navigation model for a robotic platform. The supervised model training is based on expert demonstrations generated in simulation with an existing motion planner. We demonstrate that the learned navigation model is directly transferable to previously unseen virtual and, more interestingly, real-world environments. It can safely navigate the robot through obstacle-cluttered environments to reach the provided targets. We present an extensive qualitative and quantitative evaluation of the neural network-based motion planner, and compare it to a grid-based global approach, both in simulation and in real-world experiments.
