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.

翻译：在拥挤且复杂的环境中安全高效地导航机器人仍是一项重大挑战。然而，由于这些环境的动态性和复杂性，规划机器人可跟踪的高效且无碰撞路径尤为困难。本文通过利用从2D激光雷达数据计算出的凸无障碍区域，独特地连接了机器人的感知、决策与控制过程。整体流程分为三部分：（1）提出一种利用深度强化学习的机器人导航框架，将观测空间概念化为凸无障碍区域，区别于普遍依赖原始传感器输入的方法；（2）设计基于机器人运动学限制与凸区域交集的动作空间，能够高效采样本质上无碰撞的参考点。这些动作有助于引导机器人朝目标移动，并在导航过程中与其他障碍物交互；（3）采用模型预测控制跟踪由参考点形成的轨迹，同时满足凸无障碍区域及机器人运动动力学约束。通过两组消融实验及与时间弹性带的对比实验，验证了所提改进的有效性，结果表明在拥挤复杂环境中导航性能得到提升。