NRI: Robust Stochastic Control for Agile Aerial Manipulation

点击翻译按钮获取中文摘要

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.