用神经网络动态学习不安全区域，让机器人更安全地避障。
SafeLink: Safety-Critical Control Under Dynamic and Irregular Unsafe Regions
- 用成本敏感的增量式神经网络构建控制屏障函数
- 能快速适应变化的不安全区域，计算速度显著更快
- 适合需要实时避障的机器人控制系统
控制屏障函数（CBF）为机器人系统提供了安全控制的理论基础。然而，现有方法多依赖于不安全状态区域的显式解析表达，难以应对不规则且动态变化的不安全区域。本文提出SafeLink，一种基于成本敏感增量随机向量函数链（RVFL）神经网络的新型CBF构造方法。通过设计有效代价函数，SafeLink对安全与不安全状态点赋予不同敏感度，从而消除不安全点分类中的假阴性。在构建的CBF下，建立了系统安全性和控制输入利普希茨连续性的理论保证。此外，给出了增量更新定理，支持不安全区域变化时的精确实时适应。还推导出SafeLink梯度的解析表达式，便于控制输入计算。该方法在非线性双连杆机械臂末端位置控制任务中验证。实验结果表明，方法能有效学习不安全区域并快速响应其变化，在保证系统安全到达目标位置的同时，计算速度显著优于基线方法。
原文摘要 · Abstract (English)
Control barrier functions (CBFs) provide a theoretical foundation for safety-critical control in robotic systems. However, most existing methods rely on explicit analytical expressions of unsafe state regions, which are often impractical for irregular and dynamic unsafe regions. This paper introduces SafeLink, a novel CBF construction method based on cost-sensitive incremental random vector functional-link (RVFL) neural networks. By designing a valid cost function, SafeLink assigns different sensitivities to safe and unsafe state points, thereby eliminating false negatives in classification of unsafe state points. Under the constructed CBF, theoretical guarantees are established regarding system safety and the Lipschitz continuity of the control inputs. Furthermore, incremental update theorems are provided, enabling precise real-time adaptation to changes in unsafe regions. An analytical expression for the gradient of SafeLink is also derived to facilitate control input computation. The proposed method is validated on the endpoint position control task of a nonlinear two-link manipulator. Experimental results demonstrate that the method effectively learns the unsafe regions and rapidly adapts as these regions change, achieving computational speeds significantly faster than baseline methods while ensuring the system safely reaches its target position.
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