用预测误差动态调整安全约束，让多个机器人在未知环境里安全协作。
Safe Decentralized Multi-Agent Control using Black-Box Predictors, Conformal Decision Policies, and Control Barrier Functions
- 根据预测误差自动调节安全约束强度，提升鲁棒性
- 理论证明安全边界偏差随时间平均有上限
- 适合多机器人导航、自动驾驶等高安全需求场景
针对去中心化多机器人系统中因使用不确定黑箱模型预测其他智能体轨迹而导致的安全控制挑战，本文采用近期提出的合规范式决策理论，依据观测到的预测误差动态调整基于控制屏障函数的安全约束。通过这些自适应约束合成控制器，在存在预测误差的情况下仍能平衡安全性与任务完成度。我们给出了单调函数形式的安全约束差值随时间平均值的上界。实验验证了所提方法在斯坦福无人机数据集多智能体场景下的导航性能。
原文摘要 · Abstract (English)
We address the challenge of safe control in decentralized multi-agent robotic settings, where agents use uncertain black-box models to predict other agents' trajectories. We use the recently proposed conformal decision theory to adapt the restrictiveness of control barrier functions-based safety constraints based on observed prediction errors. We use these constraints to synthesize controllers that balance between the objectives of safety and task accomplishment, despite the prediction errors. We provide an upper bound on the average over time of the value of a monotonic function of the difference between the safety constraint based on the predicted trajectories and the constraint based on the ground truth ones. We validate our theory through experimental results showing the performance of our controllers when navigating a robot in the multi-agent scenes in the Stanford Drone Dataset.
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