CAREER: Environmentally-Mediated Coordination in Natural and Robot Swarms

自动机器人群正变得无处不在，成千上万的机器人和无人机同时在从仓库到娱乐灯光秀的各个地方工作。这场技术革命使得超越机器人并行工作的观点变得至关重要，而转向真正的群体智能，即整体大于部分之和。作为概念的证明，自然群体通过在空间和时间上将信息集成和传播到其共享环境中，表现出可扩展、容错和自适应的特性。在机器人群中采用这些概念可以补充现有的控制体系结构，并可能导致系统效率低于集中式控制体系结构，但部署速度更快，成本更低，对单个故障具有弹性，从初始部署到全面部署更容易调整大小，并且可以适应不断变化的任务或环境。尽管这种类型的分布式协调越来越受欢迎，但它有许多方面，而且仍然很少被理解——特别是作为旨在实现生物水平的弹性和适应性的工程群体的设计工具。为了解决这些缺点，这个学院早期职业发展（Career）研究项目将扩展环境介导协调的概念，从在静态环境中工作的完美机器人，到包括动态环境和处理错误和硬件可靠性的现实界限的方法。这项工作将产生一个动态环境中的群体模型，该模型的作用是整合、扩散、衰减和过滤来自生物模型系统特征的信息，以及实际的机器人实验。实际验证包括集体机器人结构，蜜蜂气味羽流跟踪，以及可编程物质中菌株介导的行为。该项目进一步涉及的目标是通过包容性、共享性、在线机器人课程的新方法来确保和增加这种多样化的劳动力；面向公众的跨代外展项目；跨学科学生项目；以及为机器人、生物学和建筑学领域的研究人员举办的研讨会。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。

Autonomous robot swarms are becoming ubiquitous with thousands of robots and drones operating simultaneously in places from warehouses to entertainment light shows. This technological revolution makes it critical to look beyond robots working in parallel, and towards actual swarm intelligence where the whole is more than the sum of its parts. As a proof of concept, natural swarms exhibit scalable, error tolerant, and adaptive properties by integrating and propagation information into their shared environment over space and time. Adoption of these concepts in robot swarms can complement existing control architectures, and may result in systems that are less efficient than those with centralized control architectures, but are much faster and inexpensive to deploy, are resilient to individual failures, resize easier from initial to full-scale deployment, and can adapt to changing tasks or environments. Although gaining in popularity, this type of distributed coordination has many facets and is still poorly understood - especially as a design tool for engineered swarms that aim to achieve biological levels of resilience and adaptability. To address these shortcomings, this Faculty Early Career Development (CAREER) research project will extend upon the concept of environmentally-mediated coordination from working on perfect robots operating in static environments, to include dynamic environments and ways to deal with realistic bounds on error and hardware reliability. The work will result in a model of swarms in dynamic environments that act to integrate, diffuse, decay, and filter information derived from characterization of a biological model system, as well as practical robot experiments. Practical verification involves collective robotic construction, odor plume tracking by honey bees, and strain-mediated behaviors in programmable matter. This project further involves aims to secure and increase such a diverse workforce through novel methods for inclusive, shared, online robotics curricula; cross-generational outreach programs for the public; interdisciplinary student projects; and workshops for researchers across the fields of robotics, biology, and architecture.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.