CAREER: Solar-Powered Unmanned Aerial and Ground Vehicles for Long-Term Operation in Dynamic Environments

职业：太阳能无人机和地面飞行器，可在动态环境中长期运行

• 批准号: 1453637
• 负责人: Ran Dai
• 金额: $ 50万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-03-01 至 2018-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1453637&HistoricalAwards=false
• 关键词: CAREER; Solar; Powered; Unmanned; Aerial

Current technology in solar-powered robotic systems is limited to homogeneous robots that act independently within the constraints of their design and are subject to power limitations and inability to intelligently adapt to changeable situations. These limitations prevent the widespread adoption of solar robots in applications. This career project proposes a transformative approach to the development of next generation solar-powered robotic systems that overcome these limitations. An innovative strategy will integrate heterogeneous aerial and ground vehicle operations in order to accomplish long-duration high-efficiency missions, improve adaptability to dynamic environments, and enable the effective use of environmental but variable energy sources. Results of this work will enable societally-important technological advancements in environmental monitoring, search and rescue, surveillance, and agriculture, which will contribute to U.S. economic vitality, public health, and security. This research also provides necessary insights into general robotics applications pertaining to alleviating the dependence of robotic missions on non-renewable energy sources during long-duration operations. The use and application of solar-powered robots increases the demand for high-quality photovoltaic products, which will subsequently boost development in related technologies. This work will incorporate a hierarchy of educational activities appropriate to different groups of students, from the general public to more advanced scholars. These activities include a synergy-based education platform, industry-oriented training projects, and educational open-source software for a solar robot. In collaboration with the Experimental Program to Stimulate Competitive Research team at Iowa State University, the PI will provide public visitors, especially community college and K-12 students, with access to robotic systems.The research will contribute to a novel paradigm enabling experimentation on energy-aware long-duration autonomous multi-platform systems. Establishing advanced autonomy in solar-powered robotic systems will be accomplished by leveraging interdisciplinary methodologies in the fields of dynamic networks, distributed control, and convex optimization, and by developing novel strategies for coordinating and controlling heterogeneous aerial and ground vehicles toward mission accomplishment. These multidisciplinary methodologies will be consistently integrated to yield a new paradigm for modeling, optimization, and distributed control of solar-powered robotic systems. The project will systematically infuse solar energy into a demanding technological area, namely robotic systems, with specific applications to wide-area long-duration operations. The proposed optimization and control schemes will address fundamental problems of multi-agent dynamical systems whose performance can be improved by cooperation among internal agents.

目前太阳能机器人系统的技术仅限于同质机器人，这些机器人在其设计的限制内独立行动，并且受到功率限制并且无法智能地适应多变的情况。这些限制阻碍了太阳能机器人在应用中的广泛采用。该职业项目提出了一种变革性的方法来开发下一代太阳能机器人系统，以克服这些限制。创新战略将整合异构空中和地面车辆操作，以完成长期高效任务，提高对动态环境的适应性，并能够有效利用环境但可变的能源。这项工作的成果将推动环境监测、搜救、监视和农业方面具有社会重要性的技术进步，这将有助于美国的经济活力、公共卫生和安全。这项研究还为一般机器人应用提供了必要的见解，这些应用涉及减轻机器人任务在长期操作期间对不可再生能源的依赖。太阳能机器人的使用和应用增加了对高质量光伏产品的需求，从而带动相关技术的发展。这项工作将纳入适合不同学生群体（从普通大众到更高级的学者）的教育活动层次。这些活动包括基于协同的教育平台、面向行业的培训项目以及太阳能机器人的教育开源软件。与爱荷华州立大学刺激竞争性研究实验计划团队合作，PI 将为公众访客，特别是社区学院和 K-12 学生提供使用机器人系统的机会。该研究将有助于建立一种新的范例，使能源实验成为可能-感知长期自主多平台系统。通过利用动态网络、分布式控制和凸优化领域的跨学科方法，并开发协调和控制异构空中和地面车辆以完成任务的新策略，可以在太阳能机器人系统中建立先进的自主性。这些多学科方法将得到持续集成，以产生太阳能机器人系统建模、优化和分布式控制的新范例。该项目将系统地将太阳能注入要求苛刻的技术领域，即机器人系统，并具体应用于广域长期作业。所提出的优化和控制方案将解决多智能体动力系统的基本问题，其性能可以通过内部智能体之间的合作来提高。