CAREER: Optimal Transport-based Density-Aware Multi-Agent Exploration

在大规模环境的探索和服务中，多机器人系统可以提供比单个机器人更多的好处，例如在搜索和救援、监视和侦察、智能农业、基础设施检查、野生动物监测、天气监测和行星探测等问题上。然而，高效地部署多个机器人仍然是一个挑战。部署统一或随机覆盖给定域的机器人的传统方法不一定有效。该学院早期职业发展计划(Career)奖将支持基础研究，通过结合密度信息来开发一种新的多代理控制方法，该信息反映了覆盖该领域特定领域的优先级或重要性。从传统覆盖到自适应覆盖的这种范式转变创造了一个机会，可以在时间和资源都有限的各种特派团中最大限度地提高多代理人探索的效率。该项目的成果将包括用于野生动物监测的多智能体控制的实践演示。综合研究和教育活动将直接影响未来几代科学家和工程师，通过让研究生和本科生参与研究，并为K-12学生组织教育和推广计划，重点是未被充分代表的少数群体。本项目专注于通过使用最优运输(OT)理论作为综合多智能体轨迹的工具来提高多智能体探索的效率。OT提供了一种方法来量化两个概率密度函数(PDF)之间的距离：被选择来指示给定域的相对重要性或优先级的参考PDF和将基于多代理系统的时间平均行为形成的当前PDF。提出了一种多智能体系统的分散最优控制律，对多智能体轨迹的概率密度函数进行操作和重塑，使其尽可能接近参考概率密度函数。这个新的框架，基于OT理论的概念，将有助于促进多智能体控制的知识，以进行广泛的环境探索。该项目由CMMI-DCSD计划和既定的激励竞争研究计划(EPSCoR)联合资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Multi-robotic systems can provide many benefits over single robots in exploring and servicing large-scale environments, such as in problems of search and rescue, surveillance and reconnaissance, smart farming, infrastructure inspection, wildlife monitoring, weather monitoring, and planetary exploration. However, the deployment of multiple robots in an efficient manner remains a challenge. Traditional approaches for deploying robots that uniformly or randomly cover a given domain are not necessarily efficient. This Faculty Early Career Development Program (CAREER) award will support fundamental research to develop a new method for multi-agent control by incorporating density information that reflects the priority or importance of covering specific areas in the domain. This paradigm shift from traditional to adaptive coverage creates an opportunity to maximize the efficiency of multi-agent explorations in various missions where both time and resources are limited. The outcomes of this project will include hands-on demonstrations of multi-agent control for wildlife monitoring. The integrated research and educational activities will directly impact upcoming generations of scientists and engineers through involving graduate and undergraduate students in research and organizing education and outreach programs for K-12 students with an emphasis on underrepresented minority groups.This project focuses on improving the efficiency of multi-agent explorations by employing optimal transport (OT) theory as a tool to synthesize multi-agent trajectories. OT provides a way to quantify the distance between two probability density functions (PDFs): the reference PDF that is chosen to indicate the relative importance or priority of the given domain and the current PDF that will be formed based on the time-averaged behavior of the multi-agent system. A decentralized optimal control law for multi-agent systems will be developed to manipulate and reshape the PDF of the multi-agent trajectories to make it as close to the reference PDF as possible. This new framework, based on concepts from OT theory, will serve to advance knowledge of multi-agent control for broad environment exploration.This project is jointly funded by the CMMI-DCSD program and the Established Program to Stimulate Competitive Research (EPSCoR).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.