Collaborative Research: A Comprehensive Dynamic Search Framework for Asynchronous Multi-Objective Multi-Agent Planning

自然灾害造成的重大经济和人员损失比以往任何时候都多。这些大规模灾害发生的频率和强度的增加，给救援人员拯救生命和减轻对健康和经济的影响的英勇斗争带来了额外的压力。因此，救援人员必须快速做出关键决定。在这些情况下，信息是关键，尽快获得这些信息可以挽救生命。因此，本项目开发了能够指导多个异构代理高效搜索和获取信息的搜索方法。这种多主体规划不仅是人道主义援助救灾（HADR）任务中的问题，也是定制制造、关键基础设施管理、流行病应对、野战医院自动化建设和灾后法医学的问题。由于生命处于危险之中，所使用的方法不仅要找到最大可能找到生命的可行路径，还要在给定的响应时间内找到“最佳”可能的解决方案。因此，本项目旨在创建一个全面的框架，以解决多种物流约束下的多主体多目标规划问题。这个项目的智力优势在于研究如何耦合、部分解耦或完全解耦智能体轨迹的协调规划，从而推迟规划，直到绝对需要。因此，该工作开发了正式的保证，无论是在完备性和最优性性质方面，还是在近似界方面，对于多智能体问题的各种推广所获得的次优解。这些方法的性能将通过大规模模拟和真实机器人实验得到证实。作为实验过程的一部分，该项目还将定义相关的度量标准，以衡量新方法。该项目旨在证实，延迟规划，直到必要时，提供计算，路径质量和有效性，为多智能体路径查找问题的几个重要概括带来好处。该项目由跨部门机器人基础研究项目支持，由工程（ENG）和计算机与信息科学与工程（CISE）联合管理和资助。该奖项反映了美国国家科学基金会的法定使命，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。

Natural disasters are causing significant economic and human losses more than ever. The increase in frequency and intensity of these wide-scale disasters places additional strains on rescue workers in their heroic struggle to save lives and mitigate the impacts on health and the economy. As such, rescue workers must make critical decisions at high tempo. In these scenarios, information is key, and obtaining such information, as quickly as possible, saves lives. Therefore, this project develops search methods that can direct multiple heterogeneous agents to efficiently search and acquire information. This multi-agent planning is not just a problem in Humanitarian Assistance Disaster Relief (HADR) missions, but also in customized manufacturing, critical infrastructure management, pandemic response, automated construction of field hospitals, and post-disaster forensics. Since lives are on the line, the approaches used must not only find feasible paths that maximize the likelihood of finding life, but also finds “best” possible solutions within a given response time. Therefore, this project seeks to create a comprehensive framework to address a wide family of multi-agent multi-objective planning problems operating under several logistic constraints.The intellectual merit of this project investigates how to couple, partially decouple, or completely decouple the coordinated planning of the agent's trajectories, and therefore defer planning until absolutely needed. As such, the work develops formal guarantees, either in terms of completeness and optimality properties, or approximation bounds, for the sub-optimal solutions obtained for various generalizations of the multi-agent problem. The performance of the approaches will be corroborated through large-scale simulations, and experiments on real robots. As part of the experimental process, this project will also define relevant metrics against which the new methods will be measured. This project will aim to substantiate that deferred planning, until necessary, offers computational, as well as path quality, and efficacy, benefits for several important generalizations of multi-agent path findingproblems.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.