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

多机器人系统可以在探索和服务大规模环境方面提供许多优势，例如搜索和救援，监视和侦察，智能农业，基础设施检查，野生动物监测，天气监测和行星探索。然而，以有效的方式部署多个机器人仍然是一个挑战。传统的方法部署机器人，均匀或随机覆盖一个给定的域不一定是有效的。该学院早期职业发展计划（CAREER）奖将支持基础研究，通过结合反映该领域特定领域的优先级或重要性的密度信息，开发多智能体控制的新方法。这种从传统到适应性覆盖的范式转变为在时间和资源有限的各种任务中最大限度地提高多智能体探索的效率创造了机会。该项目的成果将包括野生动物监测多代理控制的实践演示。综合研究和教育活动将直接影响未来几代的科学家和工程师，通过参与研究生和本科生的研究和组织教育和推广计划，为K-12学生，重点是代表性不足的少数群体。该项目的重点是提高效率的多智能体探索采用最优运输（OT）理论作为工具，以合成多智能体轨迹。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：选择参考PDF以指示给定域的相对重要性或优先级，以及将基于多智能体系统的时间平均行为形成的当前PDF。一个分散的多智能体系统的最优控制律将被开发来操纵和重塑多智能体轨迹的PDF，使其尽可能接近参考PDF。这个新的框架，从OT理论的概念为基础，将有助于推进知识的多智能体控制广泛的环境exploration.This项目是共同资助的CMMI-DCSD计划和既定计划，以刺激竞争力的研究（EPSCoR）。这个奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

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.