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。这些大规模灾害的频率和强度增加，给救援人员拯救生命和减轻对健康和经济影响的英勇斗争带来了额外的压力。因此，救援人员必须以高克里思做出关键决定。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，该项目开发了可以指导多个异构代理高效搜索和获取信息的搜索方法。这种多智能体规划不仅是人道主义援助救灾（HADR）任务中的一个问题，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取证中的一个问题。由于生命危在旦夕，所采用的方法不仅必须找到最大可能找到生命的可行路径，而且还必须在给定的响应时间内找到“最佳”解决方案。因此，这个项目旨在创建一个全面的框架，以解决一个广泛的家庭的多智能体多目标规划问题的几个后勤constrain.The智力的优点，这个项目研究如何耦合，部分解耦，或完全解耦的协调规划的代理的轨迹，因此推迟规划，直到绝对需要。因此，工作开发正式的保证，无论是在完整性和最优性，或近似界，为次优的解决方案，获得各种推广的多智能体问题。这些方法的性能将通过大规模模拟和在真实的机器人上的实验得到证实。作为实验过程的一部分，该项目还将确定相关的衡量标准，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，以及路径质量，和功效，多智能体路径发现问题的几个重要概括的好处。该项目由机器人计划的跨董事会基础研究支持，由工程局（ENG）和计算机与信息科学与工程局（CISE）共同管理和资助该奖项反映了NSF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.