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

空中挂钩：使用带有电缆的空中机器人形成和控制全自动运输的挂钩

• 批准号: 2322840
• 负责人: David Saldana
• 金额: $ 59.94万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322840&HistoricalAwards=false
• 关键词: AerialHitches; Forming; Controlling; Hitches; Fully

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.

该项目将改善空中机器人，以便他们可以更好地掌握，操纵和运输物体，而无需人力干预。常规的空中机器人依靠抓地力（即最终效应器）和机器人臂来操纵和运输物体。这些零件不仅增加了重量，还减少了飞行持续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，其中机器人利用轻量级电缆来形成“挂钩”来执行操纵任务，绕开了对较重的外部组件的需求。电缆的好处是它们轻巧且灵活，从而允许传统的刚性握手无法提供的多功能操纵技术。 电缆还可以符合各种形状和大小的对象，从而增加空中机器人可以执行的任务范围。此外，使用电缆大大减少了系统的整体重量，从而增加了飞行持续时间。这种开创性的概念有可能在建筑，物流和灾难管理等领域中极大地提高生产率，在该领域，高频，重复的运输任务很常见。在这些扇区中，使用基于电缆的航空机器人系统可以降低与手动运输相关的安全风险，并在高架或危险区域中工作。在技术方面，该项目需要将通用的电缆配置抽象成有限维度的空间，以管理电缆的物理动态。增强学习算法将根据此抽象制定，通过利用理想的动力学模型来加快融合到最佳动作的融合。将开发基于拓扑的计划算法以形成链接，这是对所需的特定运动的核算。代替从头开始制定计划的，将建立一个挂钩库来简化运输过程，可以根据运输要求进行调整。 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.