基于风险自适应CVaR障碍函数的不确定拥挤环境安全导航
Safe Navigation in Uncertain Crowded Environments Using Risk Adaptive CVaR Barrier Functions
AI总结：
针对动态拥挤环境下机器人导航的不确定性挑战，提出基于CVaR-BF的风险自适应方法及动态区域障碍函数，可自适应调整风险、扩展安全裕度，性能优于现有社会导航方法。
摘要：
动态拥挤环境中的机器人导航因障碍模型的固有不确定性面临重大挑战。本文提出一种基于条件风险价值障碍函数（CVaR-BF）的风险自适应方法，其风险水平可自动调整以接受最低必要风险，在不确定性下实现了安全性与优化可行性的良好表现。此外，我们引入了一种基于动态区域的障碍函数，通过评估机器人与障碍物之间的相对状态来表征碰撞可能性。将风险自适应与该新函数结合后，我们的方法可自适应扩展安全裕度，使机器人在高动态环境中能主动规避障碍物。对比实验与消融研究表明，我们的方法优于现有社会导航方法，验证了所提框架的有效性。
英文摘要：
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.
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