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; 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高效，弹性和强大的异质机器人群体系统，该系统由无人机（无人驾驶飞机）和UGVS（无人驾驶地面车辆）组成，用于在搜索和救援，农业收获和太空探索等大型未知环境中觅食任务。有三个主要的挑战阻碍了在实践中觅食机器人群的效率。首先，尽管均匀的地面机器人群是有效的，但由于感应和移动性的能力有限，它们在搜索大面积的多个目标或资源方面面临限制。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，在处理具有不同配置的异质机器人时，这是不可扩展的。最后，机器人群中可能发生传感器故障和对抗性攻击，并可能导致级联效应，从而降低群体的鲁棒性和韧性。该项目利用机器人，机器学习和网络安全的有前途的跨学科方法，以实现与异质机器人的机器人群体的可扩展，健壮和弹性的觅食。拟议的教育计划旨在在本科和研究生级别的相关领域创建新课程，并通过机器人博览会，夏季研究营和讲习班等各种举措让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，教育和外展活动。 拟议的研究探讨了异质机器人群体系统的设计，多个最短的路径计划，联合学习（FL）和时空数据异常检测。研究计划包括三个研究推力。 1）将开发出分散的多个最短路线计划算法，以进行按需无人机感应。该算法将使无人机能够有效地感知并沿最短路线探索有趣的位置，从而可以及时收集数据，同时节省能源资源。 2）将开发去中心化联合学习（FL）算法，以支持具有异质机器人群中隐私的协作模型培训。所提出的算法允许为每个机器人自定义模型，从而使群随着机器人数量的增加而变得更加可扩展和弹性。 3）基于基于早期异常检测模块的子区域不和谐模块，以早期检测资源约束环境中的机器人故障/攻击，从而阻止了整个群体的级联效应。对异常检测的研究将确保通过FL学到的模型的弹性和鲁棒。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。