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

该项目旨在创建一个由无人机(UAV)和无人驾驶地面车辆(UGV)组成的高效、弹性和健壮的异质机器人群系统，用于在大型未知环境中进行搜救、农业收获和太空探索等觅食任务。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，虽然同构的地面机器人群体是有效的，但由于有限的感知和移动能力，它们在大范围内搜索多个目标或资源时面临着限制。其次，决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，当处理具有不同配置的异类机器人时，中央服务器是不可扩展的。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，从而降低群的健壮性和弹性。该项目利用一种前景看好的跨学科方法，横跨机器人学、机器学习和网络安全，以实现一个可扩展、健壮和弹性强的具有不同种类机器人的觅食机器人群。拟议的教育计划旨在在本科生和研究生层面创建相关领域的新课程，并通过机器人博览会、暑期研究夏令营和研讨会等各种举措，让K-12学生参与研究。它还将促进拉美裔学生参与南得克萨斯州地区的研究、教育和外联活动。该研究探讨了异质机器人群系统的设计、多最短路径规划、联邦学习和时空数据异常检测。这项研究计划包括三项研究推动力。1)针对无人机按需感知问题，提出了分布式多路径最短路径规划算法。该算法将使无人机能够有效地感知和探索最短路线上感兴趣的位置，从而在节省能源的同时及时收集数据。2)开发分布式联邦学习(FL)算法，以支持在异类机器人群中进行隐私保护的协作模型训练。所提出的算法允许为每个机器人定制一个模型，使群体随着机器人数量的增加而更具伸缩性和弹性。3)基于子轨迹不一致的早期异常检测模块，能够在资源受限的环境中及早发现机器人的故障/攻击，防止整个群体的连锁效应。异常检测的研究将确保通过FL学习的模型的弹性和健壮性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.