Data-driven distributed control of mobile robotic networks: Where machine learning meets game theory

移动机器人网络；(例如，无人驾驶飞行器机队)为公认的军事用途以及广泛的民用用途提供了扩展能力。有几个因素有助于它们日益增长的潜力和重要性。特别是，技术进步使更小的平台具有更强的传感、通信和处理能力。此外，自主行动提供了几个竞争优势，例如超过人类疲劳限制的持续监视或远程操作能力，而不需要支付资产和人员的后勤运输成本。智能优点：分布式控制成为充分发挥移动机器人网络潜力的关键。目前的分布式控制模式主要是基于模型的，不足以处理重大不确定性，包括(1)环境不确定性，即移动机器人运行的非结构化环境中的不可预见因素；(2)动态不确定性，即移动机器人物理动力学的不准确性。为了弥补这些差距，该项目将利用机器学习的一个领域强化学习和最初在经济学中发展起来的博弈论，来开发一个新的数据驱动(更具体地说，无模型)分布式控制框架。开发的框架是无模型的、完全分布式的、自治的，其性能是严格可证明的。该框架将大大提高移动机器人在面临重大环境不确定性和动态不确定性时的自主性，特别是在长期任务中。更广泛的影响：这项研究的成功完成将为能够在非结构化环境中有效运行的移动机器人网络的分析、综合和原型制作提供工程指导。这些研究成果深刻地影响了各种工程学科，包括科学数据收集、国土安全行动和智能交通系统。提出的研究是跨学科的，涉及博弈论、机器学习、控制、机器人运动规划和分布式算法之间的相互作用。这将为STEM的高中生、本科生和研究生提供跨越传统学科界限的教育和培训机会。与行业合作伙伴的合作强调了产生超越学术界的影响的潜力。

Mobile robotic networks; (e.g., fleets of unmanned aerial vehicles) offer expanded capabilities for recognized military uses as well as a wide variety of civilian uses. There are several factors that contribute to their increasing potential and importance. In particular, technological advances have enabled smaller platforms with increased sensing, communication, and processing capabilities. In addition, autonomous operations offer several competitive advantages such as persistent surveillance that exceeds human fatigue limitations or remote operation capabilities without the logistical transport costs for assets and personnel. Intellectual Merit: Distributed control becomes key to fully realize the potentials of mobile robotic networks. Current distributed control paradigms are mainly model-based and inadequate to handle significant uncertainties, including (1) environmental uncertainties; i.e., unforeseeable elements in unstructured environments where mobile robots operate; (2) dynamic uncertainties; i.e., inaccuracies of the physical dynamics of mobile robots. To bridge the gaps, this project will leverage reinforcement learning, an area of machine learning, and game theory, initially developed in economics, to develop a new data-driven (more specifically, model-free) distributed control framework. The developed framework is model-free, fully distributed, autonomous, and its performance is rigorously provable. The framework will significantly improve the autonomy of mobile robots when they face significant environmental uncertainties and dynamic uncertainties especially in long-term missions. Broader Impacts: Successful completion of this research will provide engineering guidelines in analysis, synthesis and prototyping of mobile robotic networks which can effectively operate in unstructured environments. The research findings profoundly impact a variety of engineering disciplines, including scientific data collection, homeland security operations and intelligent transportation systems. The proposed research is interdisciplinary and involves interactions among game theory, machine learning, control, robotic motion planning and distributed algorithms. This will lead to educational and training opportunities that cross traditional disciplinary boundaries for high-school, undergraduate and graduate students in STEM. The collaborations with industrial partners stress the potentials to make an impact beyond academia.