CRII: CSR: Energy-Aware Coordination and Management of Multi-Purpose Autonomous Robots for Maintained Continuity of Operations and Long Battery Lifetime.

自主机器人（AR）车队将很快协助人类执行多项任务。可能影响AR车队在我们社会中广泛采用的最重要因素之一是它们对最终用户的易于管理。该项目的目标是通过允许最终用户指示在工作期间执行的任务集，并通过实施自动(i)将任务分配给AR， （ii）协调充电时间表，（iii）确保操作的连续性，以及（iv）最大限度地减少电池退化的算法，实现AR车队的轻松管理。这个项目的智力优势集中在设计一个AR车队管理系统，最终用户可以使用该系统轻松地与AR车队进行交互。主要研究方向有：(1)联合优化自动驾驶汽车的并发任务分配和充电计划，最大限度地减少任务停机时间和电池退化；（ii）协调人工智能系统的计算和感应资源，以平衡任务性能和能源消耗；（iii）在一个小型物理试验台和一个大型模拟试验台实施完整的系统并进行试验。拟议的工作将引入可用于管理AR车队的新技术，并促进自主机器人在我们社会中的广泛使用，例如家庭、智能农场、工业和智能城市。该项目将提供一个新的硬件和软件测试平台，以帮助培养和激励本科生和研究生。将建立新的行业合作，以提高AR的采用。新的学术合作也将与各个领域的研究人员建立，包括机器人、人工智能、控制系统、传感和近似算法。所有新算法和实验结果将以期刊文章、会议记录、研讨会、邀请演讲和学生论文的形式发布。此外，数据、源代码、模拟器和硬件细节将保存在一个专门的研究网站（http://brok.eng.wayne.edu/AFM.html）上。该存储库将在项目完成后至少保留和维护五年。该奖项反映了美国国家科学基金会的法定使命，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。

Autonomous Robot (AR) fleets will soon assist humans for the execution of several tasks. One of the most important factors that will likely affect the widespread adoption of AR fleets in our society is their ease of management for the end users. The goal of this project is to achieve the easy management of the AR fleet by allowing end users to indicate the set of tasks to execute during a working period and by implementing algorithms that automatically (i) allocate tasks to ARs, (ii) coordinate recharge schedules, (iii) ensure continuity of operations, and (iv) minimize the battery degradation. The intellectual merits of this project are focused on designing an AR fleet manager system that can be used by end users to interact easily with an AR fleet. Three main research thrusts are: (i) joint optimization of concurrent tasks allocation and recharge scheduling for ARs to minimize the task downtime and battery degradation during the working period; (ii) coordination of the ARs' computing and sensing resources to balance tasks performance and energy consumption; (iii) full system implementation and experimentation on a small-scale physical testbed and a large-scale simulated testbed. The proposed work will introduce new technologies available for the management of AR fleets and boost the widespread use of autonomous robots in our society, e.g., homes, smart farms, industry, and smart cities. The project will provide a new hardware and software testbed to help train and inspire undergraduate and graduate students. New industry collaborations will be established to enhance AR adoption. New academic collaborations will also be established with researchers in various areas including robotics, artificial intelligence, control systems, sensing, and approximation algorithms. All the new algorithms and the experimental results generated as part of the proposed project will be distributed in the form of journal articles, conference proceedings, workshops, invited presentations, and student thesis. In addition, the data, source code, simulators, and hardware details will be maintained on a dedicated research website (http://brok.eng.wayne.edu/AFM.html). This repository will be retained and maintained for at least five years after the completion of the project.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.