用安全约束提升机器人共享自主的可靠性，避免碰撞。
A Safety-Aware Shared Autonomy Framework with BarrierIK Using Control Barrier Functions
- 在逆运动学层引入控制屏障函数，确保融合指令后仍安全
- 仿真与用户实验均显示碰撞时间减少，最小安全距离增加
- 适合需要高安全性的远程操控或复杂环境作业场景
共享自主将操作员意图与自主辅助结合。在密集环境中，线性融合可能导致即使各源单独安全也产生危险指令。现有方法多通过势场或代价项实现软性避障，无法提供硬性安全保障。本文在共享自主的逆运动学层引入控制屏障函数（CBFs），在保证任务性能的前提下，确保融合后的动作安全。在典型障碍环境的仿真及虚拟现实遥操作实验中验证：相比纯遥控，采用本方法可显著降低碰撞时间、提升最小安全距离。用户研究显示，参与者感知更安全、信任度更高、干扰更少，普遍偏好使用该安全过滤器。更多材料见 https://berkguler.github.io/barrierik。
原文摘要 · Abstract (English)
Shared autonomy blends operator intent with autonomous assistance. In cluttered environments, linear blending can produce unsafe commands even when each source is individually collision-free. Many existing approaches model obstacle avoidance through potentials or cost terms, which only enforce safety as a soft constraint. In contrast, safety-critical control requires hard guarantees. We investigate the use of control barrier functions (CBFs) at the inverse kinematics (IK) layer of shared autonomy, targeting post-blend safety while preserving task performance. Our approach is evaluated in simulation on representative cluttered environments and in a VR teleoperation study comparing pure teleoperation with shared autonomy. Across conditions, employing CBFs at the IK layer reduces violation time and increases minimum clearance while maintaining task performance. In the user study, participants reported higher perceived safety and trust, lower interference, and an overall preference for shared autonomy with our safety filter. Additional materials available at https://berkguler.github.io/barrierik.
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