CAREER: Secure, Resilient, and Risk-Aware Multi-Robot Coordination

职业：安全、有弹性且具有风险意识的多机器人协调

• 批准号: 1943368
• 负责人: Pratap Tokekar
• 金额: $ 54.88万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943368&HistoricalAwards=false
• 关键词: CAREER; Secure; Resilient; Risk; Aware

The overarching goal of this project is to make multi-robot systems operate reliably in real-world conditions. Most current multi-robot systems are "brittle" since the failure of a single robot may bring the whole team down. Cyber-attacks that incapacitate robots or compromise their sensors are becoming increasingly realistic. This project will focus on multi-robot coordination in scenarios where the robots operate in failure-prone or adversarial environments. The key question that the project will address is how can a team of robots carefully coordinate and adapt their actions to make them resilient to failures. The outcomes of this project will be a significant step towards the overarching vision of persistent and reliable autonomous operations in real-world conditions. These research activities will go hand-in-hand with educational and outreach activities that will integrate real-world examples of multi-robot coordination in precision agriculture and environmental monitoring to train the next generation of engineers in multi-disciplinary thinking. To broaden participation from underrepresented minorities, the project will develop a new hands-on robot programming summer camp for middle-school students. To ensure broad applicability, middle-school teachers will be trained in this workshop with the goal of them incorporating the technical activities in their classrooms. The investigator will develop a new course on multi-robot coordination and mentor undergraduate/graduate students, who are the future faculty, to create and lead a new design-oriented course through the Student Initiated Courses program. Dissemination of our findings will be achieved through workshops, articles geared toward broader audiences, and community outreach events.The main intellectual contributions of this project are in the introduction of a novel class of problems and development of new algorithms and theoretical limits for multi-robot coordination in adversarial and uncertain settings. The underlying framework will be based on submodular optimization which is an often-used technique in multi-robot coordination. Existing works that use submodular optimization for coordination typically assume that the function value can be computed exactly. The key contribution of this project will be to relax this assumption which leads to a wide variety of research problems that will be investigated. Broadly speaking, three classes of problems will be investigated: devising offline coordinated deployments that are secure against adversarial attacks; devising online, adaptive strategies that are resilient to real-time failures and attacks; and devising online and offline plans that take into account novel measures of risk in stochastic submodular optimization. Various versions of the problem such as: distributed and scalable; richer attack models particularly for distributed networks; and richer utility functions particularly those using deep neural networks, will be investigated. The project seeks to devise algorithms with constant factor approximation and constant competitive ratio guarantees.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.

这个项目的首要目标是使多机器人系统在真实世界的条件下可靠地运行。大多数当前的多机器人系统都是“脆弱的”，因为单个机器人的故障可能会导致整个团队崩溃。使机器人丧失能力或破坏其传感器的网络攻击正变得越来越现实。该项目将侧重于在机器人在容易发生故障或敌对环境中操作的情况下进行多机器人协调。该项目将解决的关键问题是，一组机器人如何仔细协调和调整它们的行动，使它们对失败具有弹性。该项目的成果将是朝着在现实世界条件下持续和可靠的自主行动这一总体愿景迈出的重要一步。这些研究活动将与教育和外联活动齐头并进，这些活动将整合精准农业和环境监测中多机器人协调的真实例子，以培训下一代工程师的多学科思维。为了扩大少数族裔的参与，该项目将为中学生开发一个新的动手机器人编程夏令营。为了确保广泛的适用性，中学教师将在这个研讨会上接受培训，目标是将技术活动融入到他们的课堂上。研究人员将开发一门关于多机器人协调的新课程，并指导本科生/研究生，他们是未来的教师，通过学生发起课程计划创建和领导一门新的面向设计的课程。我们的发现将通过研讨会、面向更广泛受众的文章和社区推广活动来实现。该项目的主要智力贡献在于引入了一类新的问题，并为对抗性和不确定环境中的多机器人协调开发了新的算法和理论限制。基本框架将基于子模块优化，这是多机器人协调中经常使用的技术。已有的使用子模优化进行协调的工作通常假定函数值可以精确计算。这个项目的主要贡献将是放松这一假设，这导致了将被调查的各种研究问题。一般而言，将调查三类问题：设计安全地抵御对手攻击的离线协调部署；设计对实时故障和攻击具有弹性的在线自适应战略；以及设计在线和离线计划，在随机子模块优化中考虑到新的风险度量。该问题的各种版本，例如：分布式和可伸缩性；更丰富的攻击模型，尤其是分布式网络；以及更丰富的效用函数，特别是那些使用深度神经网络的攻击模型，将被研究。该项目寻求设计具有恒定因子近似和恒定竞争比保证的算法。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。

项目成果

Decision-Oriented Learning with Differentiable Submodular Maximization for Vehicle Routing Problem

车辆路径问题的可微子模最大化决策导向学习

• DOI: 10.1109/iros55552.2023.10342311
• 发表时间: 2023
• 期刊: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems
• 作者: Shi, Guangyao;Tokekar, Pratap
• 通讯作者: Tokekar, Pratap

Robust Multiple-Path Orienteering Problem: Securing Against Adversarial Attacks

鲁棒多路径定向问题：抵御对抗性攻击

• DOI: 10.1109/tro.2022.3232268
• 发表时间: 2023
• 期刊: IEEE Transactions on Robotics
• 影响因子: 7.8
• 作者: Shi, Guangyao;Zhou, Lifeng;Tokekar, Pratap
• 通讯作者: Tokekar, Pratap

Risk-Aware Submodular Optimization for Multirobot Coordination

多机器人协调的风险感知子模块优化

• DOI: 10.1109/tro.2022.3158227
• 发表时间: 2022
• 期刊: IEEE Transactions on Robotics
• 影响因子: 7.8
• 作者: Zhou, Lifeng;Tokekar, Pratap
• 通讯作者: Tokekar, Pratap

Graph Neural Networks for Decentralized Multi-Robot Target Tracking

用于分散式多机器人目标跟踪的图神经网络

• 发表时间: 2022
• 期刊: Proceedings of the IEEE Conference on Decision Control
• 作者: Zhou, L;Sharma, V;Prorok, A;Ribeiro, A;Tokekar, P;Kumar, V
• 通讯作者: Kumar, V

Pred-NBV: Prediction-Guided Next-Best-View Planning for 3D Object Reconstruction

• DOI: 10.1109/iros55552.2023.10341650
• 发表时间: 2023-04
• 期刊: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Harnaik Dhami;V. Sharma;Pratap Tokekar