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.

Project Report

Normal 0 false false false EN-US X-NONE X-NONE This EAGER Project initiated research in the area of control systems design inspired from flight mechanisms and collective motions of different biological species in nature. The first research thrust involved exploring the extensive literature data base on different aerial species (mainly insects, bats & birds) to learn the simple flight mechanisms and maneuvers used by them in performing daily survival tasks such as navigation through cluttered and unknown environments, prey- capture, evasion of conspecifics, etc. The neural basis of these ‘species-specific’ maneuvers that shed light on the sensory neurons that primarily function in controlling these motor actions was also explored. It was evident from an extensive literature search that existing data on flight mechanisms and maneuvers of different insects, birds and bats are very scattered and obscure. Also, very few researchers have linked vision-based sensing (as in insects & birds) or sonar-based sensing (as in bats) to the actual flight motor actions in these species. Consequently, a need for designing and conducting a well-organized and structured set of experiments involving one or multiple aerial species to gather data that can be useful in designing novel control algorithms was identified. The second research thrust involved exploring ideas on the collective motion of aerial, aquatic and terrestrial animals such as bird flocks, bee swarms, fish schools and ant colonies to explain the existing forms of communication (if any) amongst individuals while performing a group behavior as studied by conservation biologists. Literature suggests that collective motion of most biological species in nature is perception based i.e. individuals in a group adjust their motion or behavior with respect to the motion or behavior of its neighboring individuals while executing a group task. Using guidelines such as the above, we have developed simple algorithms for controlling the motion of collectives under realistic constraints that can be applied in some specific areas of interest such as formation flying and search missions.

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University of Central Florida
United States
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