为轮式机器人设计安全平滑的导航控制，无需在线优化即可避障。
Planning Smooth and Safe Control Laws for a Unicycle Robot Among Obstacles
- 用新型二次规划构造无穷光滑向量场，减少路径弯曲和转向。
- 在强输入限制下仍能安全到达目标，速度比基线快一倍，转角控制减半。
- 适合对实时性与安全性要求高的移动机器人控制场景。
本文提出一种框架，使单轮点机器人在任意可接受状态出发，于有障碍物环境中安全导航至目标位置，同时考虑输入限制。我们引入一种新的二次规划（QP）公式，生成具有更高光滑性（C∞）、总弯曲度和总转向量更小的向量场。随后设计了一种解析的非线性反馈控制器，其天然满足Nagumo定理条件，确保安全集的前向不变性，无需任何在线优化。仿真表明，即使在严格的输入限制下，该控制器仍能安全收敛至目标。与基线方法相比，本方法实现两倍更快的到达时间，且角控制努力降低超过50%。
原文摘要 · Abstract (English)
This paper presents a framework for safe navigation of a unicycle point robot to a goal position in an environment populated with obstacles from almost any admissible state, considering input limits. We introduce a novel QP formulation to create a Cinfinity-smooth vector field with reduced total bending and total turning. Then we design an analytic, non-linear feedback controller that inherently satisfies the conditions of Nagumo's theorem, ensuring forward invariance of the safe set without requiring any online optimization. We have demonstrated that our controller, even under hard input limits, safely converges to the goal position. Simulations confirm the effectiveness of the proposed framework, resulting in a twice faster arrival time with over 50\% lower angular control effort compared to the baseline.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。