EAGER: Local Control Strategies for Predicting Emergent Behavior and Cooperative Control in Real Time under Minimal Communication
EAGER: Local Control Strategies for Predicting Emergent Behavior and Cooperative Control in Real Time under Minimal Communication
批准号:
1134669
负责人:
Suhada Jayasuriya
金额:
$17.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-09-01 至 2014-02-28
中文摘要
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英文摘要
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.
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