凸モデル予測制御と学習を組み合わせた混雑環境での衝突回避ロボットナビゲーション
Collision-Free Robot Navigation in Crowded Environments using Learning based Convex Model Predictive Control
2D LiDARから得た凸障害物なし領域を観測として強化学習し、その領域内で生成した参照点をMPCで追従することで、混雑環境でも衝突を避けて効率的に移動するナビゲーション手法を提案した。
著者: Zhuanglei Wen, Mingze Dong, Xiai Chen
分類: cs.RO
原文アブストラクト
Navigating robots safely and efficiently in crowded and complex environments remains a significant challenge. However, due to the dynamic and intricate nature of these settings, planning efficient and collision-free paths for robots to track is particularly difficult. In this paper, we uniquely bridge the robot's perception, decision-making and control processes by utilizing the convex obstacle-free region computed from 2D LiDAR data. The overall pipeline is threefold: (1) We proposes a robot navigation framework that utilizes deep reinforcement learning (DRL), conceptualizing the observation as the convex obstacle-free region, a departure from general reliance on raw sensor inputs. (2) We design the action space, derived from the intersection of the robot's kinematic limits and the convex region, to enable efficient sampling of inherently collision-free reference points. These actions assists in guiding the robot to move towards the goal and interact with other obstacles during navigation. (3) We employ model predictive control (MPC) to track the trajectory formed by the reference points while satisfying constraints imposed by the convex obstacle-free region and the robot's kinodynamic limits. The effectiveness of proposed improvements has been validated through two sets of ablation studies and a comparative experiment against the Timed Elastic Band (TEB), demonstrating improved navigation performance in crowded and complex environments.