MIDGARD: 非構造環境における自律ナビゲーションのためのシミュレーションプラットフォーム
MIDGARD: A Simulation Platform for Autonomous Navigation in Unstructured Environments
Unreal Engine を用いたフォトリアルな3D環境で、屋外の非構造環境を走行する自律ロボットのナビゲーションを学習・評価できるオープンソースのシミュレーションプラットフォームを提案。
著者: Giuseppe Vecchio, Simone Palazzo, Dario C. Guastella, Ignacio Carlucho, Stefano V. Albrecht, Giovanni Muscato, Concetto Spampinato
分類: cs.RO
原文アブストラクト
We present MIDGARD, an open-source simulation platform for autonomous robot navigation in outdoor unstructured environments. MIDGARD is designed to enable the training of autonomous agents (e.g., unmanned ground vehicles) in photorealistic 3D environments, and to support the generalization skills of learning-based agents through the variability in training scenarios. MIDGARD's main features include a configurable, extensible, and difficulty-driven procedural landscape generation pipeline, with fast and photorealistic scene rendering based on Unreal Engine. Additionally, MIDGARD has built-in support for OpenAI Gym, a programming interface for feature extension (e.g., integrating new types of sensors, customizing exposing internal simulation variables), and a variety of simulated agent sensors (e.g., RGB, depth and instance/semantic segmentation). We evaluate MIDGARD's capabilities as a benchmarking tool for robot navigation utilizing a set of state-of-the-art reinforcement learning algorithms. The results demonstrate MIDGARD's suitability as a simulation and training environment, as well as the effectiveness of our procedural generation approach in controlling scene difficulty, which directly reflects on accuracy metrics. MIDGARD build, source code and documentation are available at https://midgardsim.org/.
