Collaborative Research: CISE-MSI: DP: RI: Towards Scalable, Resilient and Robust Foraging with Heterogeneous Robot Swarms

合作研究：CISE-MSI：DP：RI：利用异构机器人群实现可扩展、有弹性和稳健的觅食

• 批准号: 2318683
• 负责人: Yuanxiong Guo
• 金额: $ 18万
• 依托单位: University of Texas at San Antonio
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318683&HistoricalAwards=false
• 关键词: Collaborative; Research; CISE; MSI; DP

The project aims to create a highly efficient, resilient, and robust heterogeneous robot swarm system composed of UAVs (unmanned aerial vehicles) and UGVs (unmanned ground vehicles) for foraging tasks in a large unknown environment such as search and rescue, agriculture harvesting, and space exploration. There are three key challenges that hinder the efficiency of foraging robot swarms in practice. Firstly, while homogeneous ground robot swarms are efficient, they face limitations in Tsearching for multiple targets or resources in a large area due to limited sensing and mobility capabilities. Secondly, machine learning models that determine robot behavior are traditionally trained on a central server, which is not scalable when dealing with heterogeneous robots with varying configurations. Finally, sensor malfunctions and adversarial attacks are likely to occur in robot swarms and can lead to cascading effects that reduce the robustness and resilience of the swarm. This project leverages a promising interdisciplinary approach across robotics, machine learning, and cybersecurity to achieve a scalable, robust, and resilient foraging robot swarm with heterogeneous robots. The proposed education plan aims to create new curricula in related fields at both the undergraduate and graduate levels and engage K-12 students in research through various initiatives such as robot expo, summer research camps, and workshops. It will also promote the participation of Hispanic students in research, education, and outreach activities in the South Texas region. The proposed research explores the design of heterogeneous robot swarm systems, multiple shortest path planning, federated learning (FL), and spatiotemporal data anomaly detection. The research plan consists of three research thrusts. 1) Decentralized multiple shortest-route planning algorithm will be developed for on-demand UAV sensing. This algorithm will enable UAVs to efficiently sense and explore interesting locations along the shortest routes, allowing for timely data collection while conserving energy resources. 2) Decentralized federated learning (FL) algorithms will be developed to support privacy-preserving collaborative model training in the heterogeneous robot swarm. The proposed algorithms allow for the customization of a model for each robot, making the swarm more scalable and resilient as the number of robots increases. 3) The sub-trajectory discord based early anomaly detection module to early detect robot failure/attacks in the resource constraint environment, preventing the cascading effect in the entire swarm. The study of anomaly detection will ensure the resilient and robust of the model learned through FL.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.

该项目旨在创建一个由无人机（UAV）和无人地面车辆（UGV）组成的高效，弹性和强大的异构机器人群系统，用于在大型未知环境中执行觅食任务，如搜索和救援，农业收获和太空探索。在实践中，有三个关键挑战阻碍了觅食机器人群的效率。首先，虽然同质地面机器人群是有效的，但由于有限的传感和移动能力，它们在大面积搜索多个目标或资源时面临限制。其次，确定机器人行为的机器学习模型传统上是在中央服务器上训练的，这在处理具有不同配置的异构机器人时是不可扩展的。最后，传感器故障和对抗性攻击很可能发生在机器人群中，并可能导致级联效应，降低群的鲁棒性和弹性。该项目利用跨机器人技术，机器学习和网络安全的有前途的跨学科方法，实现具有异构机器人的可扩展，强大和有弹性的觅食机器人群。拟议的教育计划旨在在本科和研究生阶段创建相关领域的新课程，并通过机器人博览会，夏季研究营和研讨会等各种举措让K-12学生参与研究。它还将促进西班牙裔学生参与南德克萨斯州地区的研究，教育和推广活动。 该研究探讨了异构机器人群系统的设计，多个最短路径规划，联邦学习（FL），时空数据异常检测。研究计划包括三个研究重点。1)分布式多条最短航线规划算法将被开发用于按需无人机感知。该算法将使无人机能够沿着最短路线有效地感知和探索感兴趣的位置，从而在节省能源的同时及时收集数据。2)分散式联邦学习（FL）算法将被开发，以支持隐私保护的异构机器人群中的协作模型训练。所提出的算法允许为每个机器人定制一个模型，使群体更具可扩展性和弹性的机器人数量的增加。3)基于子轨迹不一致的早期异常检测模块，在资源受限的环境下，对机器人故障/攻击进行早期检测，防止整个群体中的级联效应。异常检测的研究将确保通过FL学习的模型的弹性和鲁棒性。该奖项反映了NSF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。