CAREER: Driving the Future: Models and Control Methods to Coordinate Fleets of Self-Driving Vehicles in Future Transportation Networks

这个教师早期职业发展（CAREER）计划项目推进了对机器人网络的建模，分析和控制的科学知识，机器人网络由无人驾驶车辆组成，以协调的方式自主运行，以满足服务请求，如人员或货物的运输。 为了有效地工作，这些系统必须克服分配和调度的挑战，在实践中，这些挑战可能会造成备份、不可接受的等待时间和有害的级联效应。本计画将在空间排队论的框架内解决问题，并研究理论模型和实时控制方法，以最佳地分配车辆来满足服务请求。理论和控制算法将应用于自主移动按需系统的设计，系统范围的控制和经济评估。这些系统代表了一种变革性的、快速发展的交通模式，电动自动驾驶穿梭车可以运送城市乘客，并为无法或不愿意开车的人提供出行选择。该项目的结果将有利于美国经济，促进清洁和高效的未来交通系统，并解决21世纪世纪的流动需求。更广泛地说，这项研究适用于一大类机器人协调问题，并将对自动化供应链、物流和国家安全等几个关键领域产生积极影响。全尺寸自动航天飞机的实验将有助于扩大代表性不足的群体在研究中的参与，并促进网络物理系统的工程教育。目前用于控制机器人网络的方法是有限的，特别是在预测精度和具有正式性能保证的控制合成方面。空间排队论认为动态系统包括（i）收集由外源动态过程产生的服务请求的空间本地化队列，以及（ii）在给定网络拓扑中的队列之间行进的机器人服务车辆。因此，空间排队理论模型的机器人协调问题的各种各样的自主移动按需系统作为一个相关的例子。该项目将通过利用随机网络优化的最新算法技术来推进该领域的知识，以生成可证明正确的工具，用于对日益复杂和现实的空间排队系统进行建模，分析和控制。具体而言，该奖项支持基础研究：1）通过设计复杂设置中易于处理的分析方法来推进空间排队系统的理论，2）生成具有性能保证的控制方法，用于机器人车辆服务请求的最佳分配，以及3）将理论和控制方法应用于自主移动性按需系统的控制，通过案例研究和在全尺寸测试床上部署算法。

This Faculty Early Career Development (CAREER) Program project advances scientific knowledge on the modeling, analysis, and control of robotic networks consisting of unmanned vehicles autonomously operating in a coordinated fashion to fulfill service requests such as the transportation of people or goods. To work efficiently, such systems must overcome allocation and scheduling challenges that, in practice, can create backups, unacceptable wait times, and detrimental cascade effects. This project will cast the problem within the framework of spatial queuing theory, and investigate theoretical models and real-time control methods to optimally allocate vehicles to service requests. Theory and control algorithms will be applied for the design, system-wide control, and economic assessment of autonomous mobility-on-demand systems. Such systems represent a transformative, rapidly developing mode of transportation where electric, self-driving shuttles transport urban passengers and provide a mobility option to people unable or unwilling to drive. The results of this project will benefit the U.S. economy by fostering clean and efficient future transportation systems and addressing 21st century mobility needs. More broadly, this research is applicable to a large class of robotic coordination problems and will positively impact several critical sectors including automated supply chains and logistics and national security. Experiments on full-scale autonomous shuttles will help broaden the participation of underrepresented groups in research and catalyze engineering education on cyber-physical systems. Current methods for controlling robotic networks are limited, particularly with respect to predictive accuracy and control synthesis with formal performance guarantees. Spatial queuing theory considers dynamic systems consisting of (i) spatially-localized queues that collect service requests generated by an exogenous dynamical process, and (ii) robotic service vehicles traveling among queues in a given network topology. As such, spatial queuing theory models a large variety of robotic coordination problems, with autonomous mobility-on-demand systems as a relevant example. The project will advance knowledge in the field by leveraging recent algorithmic techniques from stochastic network optimization to generate provably-correct tools for the modeling, analysis, and control of spatial queuing systems of increasing complexity and realism. Specifically, this award supports fundamental research to 1) advance the theory of spatial queuing systems, by devising methods for tractable analyses in complex setups, 2) generate control methods with performance guarantees for the optimal assignment of robotic vehicles to service requests, and 3) apply theory and control methods to the control of autonomous mobility-on-demand systems, through case studies and the deployment of algorithms on full scale test beds.