EAGER: Local Control Strategies for Predicting Emergent Behavior and Cooperative Control in Real Time under Minimal Communication

这个探索性研究（EAGER）项目的早期概念赠款的目标是在三个领域开展探索性工作，这些领域受到鸟类，蝙蝠和昆虫等鸟类物种在极其杂乱和复杂的环境中优雅和快速飞行的卓越能力的启发：(i)研究蝙蝠和其他鸟类物种的大型数据库，以确定一套关键实验，（ii）使用收集的信息来指导包括编队飞行和搜索任务在内的一些特定场景中新控制原则的创新，（iii）研究纯粹的局部控制，以解释保育生物学家研究的数个选定的蜂群行为，并以不断演变的概念为平台，帮助更好地理解涌现行为，以及（B）综合用于在现实约束下控制集体运动的基于简单数据的算法。具体的方法将涉及（a）将集体视为连续体，（B）无碰撞运动的同伦方法，以及（c）导致有限维搜索的逐点优化，从而消除了求解复杂的两点边值问题的需要。这项研究的成功完成将表明，鸟类和其他生物的灵感可能会导致导航和控制范式的转变。这项工作的一个关键影响预计将是更好地了解视觉和声纳如何被生物系统用于在高度复杂，非结构化和混乱的环境中导航。使用机器学习思想的图像处理的当前技术状态对于真实的时间计算来说简直太难了，并且需要新的方法。如果成功的话，这项研究可以回答的问题，如：如何简单的错误校正的想法，采用基于视觉的导航？可以并行算法的发展与保证收敛时间的基础上的生物学原理？此外，将开发一个异构的移动的代理平台，其中使用生物启发控制，可用于激励大学预科和本科生从事工程职业。研究结果将通过发表论文、在会议上发言和举办一个生物启发控制设计讲习班来传播。

The objective of this Early-Concept Grants for Exploratory Research (EAGER) project is to initiate exploratory work in three areas inspired by the remarkable ability of avian species such as birds, bats and insects to gracefully and rapidly fly through extremely cluttered and complex environments.: (i) research the large data base on bats and other avian species to identify a set of key experiments, (ii) use information gathered to guide the innovation of new control principles in a few specific scenarios including formation flying and search missions, (iii) investigate purely local control to explain a few selected swarm behaviors studied by conservation biologists and use the evolving ideas as a platform to (a) help better understand emergent behavior, and (b) synthesize simple data based algorithms for controlling the motion of collectives under realistic constraints. Specific approaches to be followed will involve (a) treating collectives as continua, (b) homotopy methods for collision free motion, and (c) pointwise optimization leading to finite dimensional searches that eliminates the need for solving complex two-point boundary value problems. The successful completion of this research will show that avian and other biological inspirations could lead to a shift in paradigm in navigation and control. A key impact of this work is expected to be a better understanding of how vision and sonar are used by biological systems to navigate in highly complex, unstructured and cluttered environments. The current state of the art of image processing using ideas of machine learning is simply too difficult for real time computations and new approaches are needed. If successful this research can answer questions such as: How can simple error correction ideas be adopted for vision based navigation? Can parallel algorithms be developed with guarantees of convergence time that are based on biological principles? Additionally, a heterogeneous mobile agent platform where bio-inspired controls are used will be developed which can be used to inspire pre-college and undergraduate students to take up careers in engineering. The results will be disseminated through publication of papers, conference presentations and a workshop to be organized in bio-inspired control design.