用自适应风险屏障函数实现复杂人群中的安全导航
Safe Navigation in Uncertain Crowded Environments Using Risk Adaptive CVaR Barrier Functions
- 基于条件风险价值的动态屏障函数，自动调节风险容忍度
- 在不确定环境中实现高安全性和优化可行性，避免碰撞
- 适合需要高安全性的人机交互与自动驾驶场景
机器人在动态、拥挤环境中的导航面临障碍物模型不确定性带来的挑战。本文提出一种基于条件风险价值屏障函数（CVaR-BF）的自适应风险方法，通过自动调整风险水平以接受最低必要风险，在不确定性下实现了安全与优化可行性的良好平衡。此外，引入一种基于动态区域的屏障函数，通过评估机器人与障碍物间的相对状态来表征碰撞可能性。结合风险自适应机制，该方法可动态扩展安全裕度，使机器人在高度动态环境中主动规避障碍物。对比实验与消融研究验证了本方法在现有社交导航方法中的优越性，并证实了所提框架的有效性。
原文摘要 · Abstract (English)
Robot navigation in dynamic, crowded environments poses a significant challenge due to the inherent uncertainties in the obstacle model. In this work, we propose a risk-adaptive approach based on the Conditional Value-at-Risk Barrier Function (CVaR-BF), where the risk level is automatically adjusted to accept the minimum necessary risk, achieving a good performance in terms of safety and optimization feasibility under uncertainty. Additionally, we introduce a dynamic zone-based barrier function which characterizes the collision likelihood by evaluating the relative state between the robot and the obstacle. By integrating risk adaptation with this new function, our approach adaptively expands the safety margin, enabling the robot to proactively avoid obstacles in highly dynamic environments. Comparisons and ablation studies demonstrate that our method outperforms existing social navigation approaches, and validate the effectiveness of our proposed framework.
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。