未知環境における俊敏な自律ロボットナビゲーションとリカバリのためのGPベースのロバスト運動計画フレームワーク
A GP-based Robust Motion Planning Framework for Agile Autonomous Robot Navigation and Recovery in Unknown Environments
ガウス過程を用いて将来の運動計画失敗リスクを予測し、リスクが高い場合に安全な状態へ回復する行動をトリガーするフレームワークを提案。シミュレーションのみで訓練し、実環境の異なるロボットで俊敏な動きを実現。
著者: Nicholas Mohammad, Jacob Higgins, Nicola Bezzo
分類: cs.RO, cs.LG
原文アブストラクト
For autonomous mobile robots, uncertainties in the environment and system model can lead to failure in the motion planning pipeline, resulting in potential collisions. In order to achieve a high level of robust autonomy, these robots should be able to proactively predict and recover from such failures. To this end, we propose a Gaussian Process (GP) based model for proactively detecting the risk of future motion planning failure. When this risk exceeds a certain threshold, a recovery behavior is triggered that leverages the same GP model to find a safe state from which the robot may continue towards the goal. The proposed approach is trained in simulation only and can generalize to real world environments on different robotic platforms. Simulations and physical experiments demonstrate that our framework is capable of both predicting planner failures and recovering the robot to states where planner success is likely, all while producing agile motion.
