NRI: Robust Stochastic Control for Agile Aerial Manipulation

一种新型的飞行机器人不仅可以导航和观察周围的环境，还可以到达和操纵人类难以到达的地方的物体。这种系统将帮助人们在不安全或偏远的地方进行操作，并将自动化手工劳动密集型任务，如包裹递送、农业检查和基础设施维修。目前的航空机械臂原型缺乏保证此类操作的可靠性和效率的控制保真度。为了克服这些限制，提出的项目开发新的控制技术，利用飞行器的能力。如果成功，这个研究项目将使在极端环境中灵活和安全的空中操作成为可能，目前使用标准方法是不可能或不可行的。本研究的目标是实现具有内置鲁棒性的机器人的规划和控制方法，这些机器人可以在自主和人类辅助模式下与环境交互和操纵。这是通过将耦合感知控制问题作为统计学习问题和自适应计算决策策略来优化未来性能和最小化安全违规概率来实现的。该方法的核心是一种可证明稳定的自适应控制方法，该方法在最大期望代价和碰撞概率方面具有概率鲁棒性保证。这些边界对应于通过贝叶斯近似正确分析得出的测量浓度不等式。两个实验平台提供了概念验证：1)自动“空气抓手”，用于重复性任务，如负载传递，作物采样和远程清洁；2)协同机器人“空中之手”，直接协助人类操作员进入危险或难以进入的地方，例如在极端环境中进行检查和维修，在救援或安全敏感任务期间。实现的技术是普遍适用的，并将作为开源ros兼容软件发布。

A new class of flying robots are beginning to, not only navigate and observe, their surroundings, but also reach and manipulate objects in places that are difficult for humans to go. Such systems will assist people through manipulation in unsafe or remote locations, and will automate manual labor-intensive tasks such as package delivery, agricultural inspection, and infrastructure repair. Current aerial manipulator prototypes lack the control fidelity to ensure reliability and efficiency that is expected from such operations. To overcome these limitations, the proposed project develops novel control techniques that exploit the capabilities of the aerial vehicle. If successful, this research project will enable agile and safe aerial manipulation in extreme environments that is presently impossible or infeasible using standard methods. The goal of this research is the realization of planning and control methods with built-in robustness for robots that can interact with and manipulate the environment in autonomous and human-assisted modes. This is accomplished by posing the coupled perception-control problem as a statistical learning problem and adaptively computing decision policies to optimize future performance and minimize probability of safety violation. At the core of the approach lies a provably-stable adaptive control methodology equipped with probabilistic robustness guarantees in terms of maximum expected cost and probability of collision. These bounds correspond to concentration-of-measure inequalities derived through Bayesian probably-approximately-correct analysis. Two experimental platforms provide proof-of-concept for: 1) an autonomous "Air-gripper" for repetitive tasks such as load delivery, crop sampling, and remote cleaning; 2) co-robotic "hands in the sky" in direct assistance to a human operator enabling access to dangerous or difficult-to-access places, e.g. for inspection and repair in extreme environments, during rescue or security-sensitive missions. The implemented techniques are generally applicable and will be released as open-source ROS-compatible software.