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，使它们能够更好地抓住、操纵和运输物体，而不需要人工干预。传统的空中机器人依靠夹爪(即末端执行器)和机械臂来操纵和运输物体。这些部件不仅增加了显著的重量，还缩短了飞行时间，并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，即机器人利用轻巧的绳索形成“搭便器”来执行操作任务，而不需要沉重的外部部件。缆索的好处是它们重量轻且灵活，允许使用传统刚性夹具无法提供的多种操作技术。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，使用电缆极大地减轻了系统的总体重量，从而延长了飞行时间。这一开创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率，在这些领域，高频、重复的运输任务很常见。在这些领域，使用基于电缆的空中机器人系统可以减少与人工运输和在高空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。在此抽象的基础上，提出一种强化学习算法，利用理想的动力学模型加速收敛到最优动作。将开发基于拓扑的规划算法来形成搭便车，并考虑到所需的特定运动。不是从头开始制定计划，而是建立一个搭便车库来简化运输流程，可以根据运输需求进行调整。通过这个项目，潜在的贡献包括在控制交错电缆的强化学习方面的进步，在空中机器人中使用拓扑框架对电缆进行建模，以及进步我们对空中机器人行为和操作原则的理解。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.