Abstract
Diverse taxa use Earth’s magnetic field in conjunction with other sensor modes to accomplish navigation tasks that range from local homing to long-distance migration across continents and ocean basins. However, despite extensive research, animal magnetoreception remains a poorly understood, and active research area. Concurrently, Earth’s magnetic field offers a signal that engineered systems can leverage for navigation and localization in environments where man-made systems such as GPS are either unavailable or unreliable. Using a proxy for Earth’s magnetic field, and inspired by migratory animal behavior, this work implements behavioral strategies to navigate through a series of magnetic waypoints. The strategies are able to navigate through a closed set of points, in some cases running through several “laps”. Successful trials were observed in both a range of environmental parameters, and varying levels of sensor noise. The study explores several of these parameter combinations in simulation, and presents preliminary results from a version of the strategy implemented on a mobile robot platform. Alongside success, limitations of the simulated and hardware algorithms are discussed. The results illustrate the feasibility of either an animal, or engineered platform to use a set of waypoints based on the magnetic field to navigate. Additionally, the work presents an engineering/quantitative biology approach that can garner insight into animal behavior while simultaneously illuminating paths of development for engineered algorithms and systems.
The rights of this work are transferred to the extent transferable according to title 17 U.S.C. 105.
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Acknowledgments
We thank Dr. Kevin Brink (AFRL/RWWI) for the use of his robotics laboratory for the hardware portion of this study.
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Taylor, B.K., Huang, G. (2017). Bioinspired Magnetic Navigation Using Magnetic Signatures as Waypoints. In: Mangan, M., Cutkosky, M., Mura, A., Verschure, P., Prescott, T., Lepora, N. (eds) Biomimetic and Biohybrid Systems. Living Machines 2017. Lecture Notes in Computer Science(), vol 10384. Springer, Cham. https://doi.org/10.1007/978-3-319-63537-8_5
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DOI: https://doi.org/10.1007/978-3-319-63537-8_5
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