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: 2318682
• 负责人: Qi Lu
• 金额: $ 42万
• 依托单位: The University of Texas Rio Grande Valley
• 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=2318682&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.

该项目旨在打造一个由UAV（无人机）和UGV（无人地面车辆）组成的高效、弹性、鲁棒的异构机器人群系统，用于在大面积未知环境中执行搜救、农业收割等觅食任务。太空探索。在实践中，阻碍觅食机器人群效率的三个关键挑战。首先，虽然同质地面机器人群很有效，但由于感知和移动能力有限，它们在大范围内搜索多个目标或资源时面临局限性。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，在处理具有不同配置的异构机器人时，该模型不可扩展。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跨学科方法，通过异构机器人实现可扩展、稳健且有弹性的觅食机器人群。拟议的教育计划旨在为本科生和研究生阶段的相关领域创建新课程，并通过机器人博览会、夏令营和研讨会等各种举措吸引 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地区的研究、教育和外展活动。 该研究探索了异构机器人群系统的设计、多重最短路径规划、联邦学习（FL）和时空数据异常检测。该研究计划由三个研究重点组成。 1）将开发分散式多重最短路线规划算法，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高效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）将开发去中心化联邦学习（FL）算法来支持异构机器人群中的隐私保护协作模型训练。所提出的算法允许为每个机器人定制模型，随着机器人数量的增加，使得群体更具可扩展性和弹性。 3）基于子轨迹不一致的早期异常检测模块，可以在资源有限的环境中及早检测机器人故障/攻击，防止整个群体的级联效应。异常检测的研究将确保通过 FL 学习的模型具有弹性和稳健性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。