RI: Small: Robotic Path Planning to Reveal Wireless Rays - A New Foundation for the Optimization of Networked Robotic Operations

机器人网络可以在许多不同的领域产生巨大的影响，例如应急响应、国家安全和移动的服务提供。 在这种系统中，一组无人驾驶车辆的任务是收集信息和合作，以完成给定的使命。无线通信是这种系统的组成部分，因为无人驾驶车辆需要连接到其他节点或远程操作员，以便传输传感数据和/或接收控制命令。由于机器人的路径直接影响其链路质量，因此无人车在路径规划过程中需要考虑通信质量。这就要求无人驾驶车辆能够准确预测其在未访问位置的链路质量。然而，由于信道功率的高空间变化，准确地预测信道质量是一个相当具有挑战性的问题，并且是一个开放的问题。该项目通过开发新的预测无线信道模型及其相应的传感和路径规划协同优化，实现了无人驾驶车辆团队的强大合作，这可能会对机器人领域产生重大影响。 该项目还包括一个教育部分，利用机器人技术和通信技术的吸引力，接触年轻人的思想。该项目为无人驾驶车辆的强大网络操作开发了一个新的多学科范式。 第一项研究任务表明，机器人可以仅根据其机载接收信道功率测量来揭示入射无线射线的构成。更具体地说，它提出了一种新的路径规划和信息处理的组合，可以揭示光线的构成，使机器人能够了解更多的信道比测量的接收功率。这种新方法对机器人现场操作具有重要意义。更具体地说，第二个主要任务建立在第一个任务的基础上，以开发一个新的机器人通道预测框架。第三个主要任务是开发一种多学科设计，通过传感和路径规划决策适当地共同优化新的预测信道模型，以实现无人驾驶车辆的鲁棒网络化操作。最后，在机器人试验台上对整个新的设计范例进行测试。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

A robotic network can have a tremendous impact in many different areas such as emergency response, national security, and mobile service provisioning. In such systems, a team of unmanned vehicles are tasked with information gathering and cooperation to accomplish a given mission. Wireless communication is an integral part of such a system as an unmanned vehicle needs to connect to other nodes or to remote operators in order to transfer sensing data and/or receive control commands. Since a robot’s path directly affects its link quality, an unmanned vehicle needs to take the communication quality into account during path planning. This then requires an unmanned vehicle to have an accurate prediction of its link quality at unvisited locations. Accurately predicting the channel quality, however, is a considerably challenging problem and an open one, due to the high spatial variations of the channel power. This project enables the robust cooperation of a team of unmanned vehicles by developing new predictive wireless channel models and its corresponding co-optimization with sensing and path planning, which can significantly impact the area of robotics. The project also has an educational component, leveraging appeals of both robotics and communications, to reach out to the young minds.This project develops a new multi-disciplinary paradigm for the robust networked operation of unmanned vehicles. The first research task shows it possible for the robot to reveal the makeup of the incoming wireless rays, based only on its onboard received channel power measurements. More specifically, it proposes a novel combination of path planning and information processing that can reveal the makeup of the rays, enabling the robot to learn considerably more about the channel than the measured received power. This new approach has a significant implication for robotic field operation as the project addresses. More specifically, the second major task builds on the first one in order to develop a new framework for robotic channel prediction. The third major task then develops a multi-disciplinary design that properly co-optimizes the new predictive channel models with sensing and path planning decisions, in order to enable the robust networked operation of unmanned vehicles. Finally, the overall new design paradigm is tested on a robotic testbed.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.