让阿克曼转向机器人在未知环境里安全避障，靠找最大空位定方向。
Safe Local Navigation for Ackermann-Steered Robots in Unmapped Environments
- 根据前方最大空位确定最安全行驶方向。
- 通过凸二次优化最大化车辆与障碍物间距，生成左右边界线。
- 适合无人车在无地图环境下实时避障，代码开源可复现。
针对无全局目标、无地图的未知环境中配备阿克曼转向系统的移动机器人，提出一种安全局部导航控制框架。基于局部障碍物检测，沿前方最大空域方向确定最安全航向，并以此构建车辆左右两侧的边界线，实现障碍物分离。边界线通过求解一个凸二次优化问题获得，旨在最大化车辆与障碍物之间的间隙。可选地，对边界线施加约束以保持平行性并平滑前一控制步的突变。随后采用反馈线性化控制器调节车辆到一条或两条边界线的距离，有效跟踪一条局部参考路径，该路径通过最大化障碍物间隙来保障安全性。实验结果表明，该方法相比部分基于探索的规划器，在生成更安全路径的同时显著缩短计算时间。本文提供开源代码用于该控制方案的应用。
原文摘要 · Abstract (English)
A control framework is proposed for safe local navigation of mobile robots equipped with Ackermann steering in unmapped environments where a global goal is absent. Based on local obstacle detections, the safest heading angle is determined along the direction of the largest open space ahead of the vehicle. Guided by this direction, bounding lines are constructed on the left and right sides of the vehicle to achieve obstacle separation. These bounding lines are obtained by solving a convex quadratic optimization that maximizes vehicle-to-obstacle clearance. Optionally, conditions are imposed on the bounding lines to preserve parallelism and smooth abrupt changes from prior control steps. A feedback-linearizing controller is then used to regulate the vehicle's distance from one or both bounding lines, effectively enabling tracking of a local reference path that preserves safety through obstacle clearance maximization. Open-source code is included for the application of this control scheme. Experimental results demonstrate that the proposed method produces safer navigation paths with significantly shorter computation times, compared to some existing exploration-based planners.
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