用动态障碍物约束保障机器人在未知环境中的安全导航
Safe Navigation in Unmapped Environments for Robotic Systems with Input Constraints
- 通过局部感知构建时变安全屏障函数，融合最新障碍信息
- 实现速度与输入受限下避障，仿真中非完整机器人成功通行
- 适合需实时避障的移动机器人系统，尤其复杂未知场景
本文提出一种基于复合控制屏障函数（CBF）的方法，用于在存在输入和状态约束条件下，对未知环境中的机器人进行导航与控制。利用实时感知反馈（如激光雷达）在线构建局部CBF，以建模环境中未知障碍带来的局部安全约束。采用软最大函数将最近N个局部CBF合成一个时变的统一屏障函数。通过控制动力学将输入约束转化为控制器-状态约束，并使用软最小函数将该约束与描述未知环境的时变CBF组合，生成一个松弛后的单一复合屏障函数。该函数被用于约束优化，求解满足状态与输入双重约束的最优控制指令。通过配备激光雷达的非完整地面机器人在未知环境中的仿真验证，结果表明机器人能有效避开未知障碍物，同时满足速度与输入约束，实现安全导航。
原文摘要 · Abstract (English)
This paper presents an approach for navigation and control in unmapped environments under input and state constraints using a composite control barrier function (CBF). We consider the scenario where real-time perception feedback (e.g., LiDAR) is used online to construct a local CBF that models local state constraints (e.g., local safety constraints such as obstacles) in the a priori unmapped environment. The approach employs a soft-maximum function to synthesize a single time-varying CBF from the N most recently obtained local CBFs. Next, the input constraints are transformed into controller-state constraints through the use of control dynamics. Then, we use a soft-minimum function to compose the input constraints with the time-varying CBF that models the a priori unmapped environment. This composition yields a single relaxed CBF, which is used in a constrained optimization to obtain an optimal control that satisfies the state and input constraints. The approach is validated through simulations of a nonholonomic ground robot that is equipped with LiDAR and navigates an unmapped environment. The robot successfully navigates the environment while avoiding the a priori unmapped obstacles and satisfying both speed and input constraints.
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