AerialHitches: Forming and Controlling Hitches for Fully Autonomous Transportation Using Aerial Robots with Cables

该项目将改进空中机器人，使它们能够在没有人为干预的情况下更好地抓取、操纵和运输物体。传统的空中机器人依赖于抓取器（即，末端执行器）和用于操纵和运输物体的机器人臂。这些部件不仅增加了机器人的重量，还缩短了飞行时间，使机器人的控制变得复杂。该项目引入了一种创新的替代方案，其中机器人利用轻质电缆形成“挂钩”来执行操作任务，从而绕过了对沉重外部组件的需求。电缆的好处是它们重量轻、灵活，允许传统刚性夹持器无法提供的多功能操作技术。 电缆还可以适应各种形状和大小的物体，增加了空中机器人可以执行的任务范围。此外，使用电缆大大降低了系统的总重量，从而增加了飞行时间。这一突破性的概念有可能极大地提高建筑、物流和灾害管理等领域的生产力，这些领域的高频率、重复性运输任务很常见。在这些领域，使用基于电缆的空中机器人系统可以降低与人工运输和在高架或危险区域工作相关的安全风险。在技术方面，该项目需要将常见的电缆配置抽象到有限维空间中，以管理电缆的物理动力学。强化学习算法将基于这种抽象来制定，通过利用理想的动力学模型来加速收敛到最佳动作。将开发基于拓扑的规划算法来形成牵引，并考虑所需的特定运动。代替从头开始创建计划，将建立一个故障库，以简化运输过程，并可根据运输要求进行调整。通过该项目，潜在的贡献包括在控制交错电缆的强化学习方面取得进展，使用空中机器人的拓扑框架对电缆进行建模，以及提高我们对空中机器人行为和操纵原理的理解。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project will improve aerial robots so they can grasp, manipulate and transport objects better and without human intervention. Conventional aerial robots rely on grippers (i.e., end effectors) and robotic arms for manipulating and transporting objects. These parts not only add significant weight, but also reduce flight duration and complicate the control of the robots. This project introduces an innovative alternative, wherein the robots utilize lightweight cables to form 'hitches' for performing manipulation tasks, bypassing the need for weighty external components. The benefit of cables is that they are lightweight and flexible, allowing for versatile manipulation techniques that traditional rigid grippers cannot provide. Cables can also conform to objects of various shapes and sizes, increasing the range of tasks an aerial robot can perform. Additionally, using cables dramatically reduces the overall weight of the system, resulting in increased flight duration. This groundbreaking concept has the potential to greatly improve productivity in fields such as construction, logistics, and disaster management, where high-frequency, repetitive transportation tasks are common. In these sectors, using a cable-based aerial robotic system could reduce safety risks associated with manual transportation and work in elevated or hazardous areas.On a technical front, the project entails abstracting common cable configurations into a finite-dimensional space to manage the physical dynamics of the cables. A reinforcement learning algorithm will be formulated based on this abstraction, expediting the convergence to optimal actions by utilizing an ideal dynamics model. Topology-based planning algorithms will be developed to form the hitches, accounting for the specific movements required. In lieu of creating plans from scratch, a library of hitches will be established to streamline the transportation process, which can be adjusted according to transportation requirements. Through this project, the potential contributions include advancements in reinforcement learning for controlling interlaced cables, modeling cables using topological frameworks in aerial robotics, and progressing our understanding of the principles governing aerial robot behavior and manipulation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.