What do animal brains have in common with a swarm of robots? 

In an effort to improve robotic swarming algorithms, an interdisciplinary team of scientists will study how the brain allows an animal to navigate and change its route while moving.

The scientists, from the Johns Hopkins University School of Medicine and Applied Physics Laboratory, will combine research into navigational planning in brains with autonomous robotic swarms to drive advances in both fields. The study will use new information and discoveries about how the brain allows an animal to navigate and change its routes while moving—called dynamic replanning—to improve swarming algorithms to the point that groups of robots will automatically adapt to changes in the environment in the same way that a rat knows which detour to take around an unexpected obstacle.

In turn, the neuroscientists will examine the replanning behaviors of drone swarms to evaluate their models of how the rodent brain dynamically replans paths, which will lead to new avenues for neuroscience research.

In early 2018, the project was awarded expanded R&D funding; encouraging simulations produced by Hwang and Schultz from that study led Zhang and the APL team to submit their proposal, “Spatial Intelligence for Swarms Based on Hippocampal Dynamics,” for an NSF grant. The key realization of the NSF proposal was the need to include an additional emergent phenomenon that occurs in the hippocampus, called sharp waves, that has been hypothesized to contribute to navigational planning in mammals. (Additionally, a new, related project led by Hwang has also received additional R&D funding from APL.)

The team envisions that this new approach to navigation will enable the kinds of tasks that society will be increasingly asking robots to perform—disaster relief and search and rescue, in addition to research and defense applications. These tasks require improved and more intelligent spatial coordination among many robots spread over large geographical areas. The team hopes that their research will create a revolutionary algorithmic framework for autonomous behaviors in swarming, as well as informing theoretical advances in understanding the brain.

For further info, please visit the website of Johns Hopkins University

The abstract of the proposal is as following: (source: www.nsf.gov)

NCS-FO: Spatial Intelligence for Swarms Based on Hippocampal Dynamics

NSF Org: IIS
Div Of Information & Intelligent Systems
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Initial Amendment Date: September 5, 2018
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Latest Amendment Date: September 5, 2018
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Award Number: 1835279
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Award Instrument: Standard Grant
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Program Manager: Kenneth C. Whang
IIS Div Of Information & Intelligent Systems
CSE Direct For Computer & Info Scie & Enginr
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Start Date: October 1, 2018
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End Date: September 30, 2020 (Estimated)
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Awarded Amount to Date: $997,996.00
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Investigator(s): Kechen Zhang kzhang4@jhmi.edu (Principal Investigator)
Grace Hwang (Co-Principal Investigator)
Marvin Carr (Co-Principal Investigator)
Kevin Schultz (Co-Principal Investigator)
Robert Chalmers (Co-Principal Investigator)
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Sponsor: Johns Hopkins University
1101 E 33rd St
Baltimore, MD 21218-2686 (443)997-1898
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NSF Program(s): IntgStrat Undst Neurl&Cogn Sys
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Program Reference Code(s): 8089, 8091, 8551
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Program Element Code(s): 8624
ABSTRACT

This project brings together theories of brain functions and principles of robotic swarm control to develop smarter swarms and to better understand the neural processes underlying spatial representations, navigation, and planning. Our world is constantly changing, and mammals have evolved the cognitive ability to plan new paths or new strategies as needed. By contrast, autonomous robots are less robust, and often have difficulty operating in complex, changing environments. This research project is grounded in the idea that individual robots in a group can be thought of analogously to neurons in an animal’s brain, which interact with one another to form dynamic patterns that collectively signal locations in space and time relative to brain rhythms. This distribution of information across space and time will enable a new paradigm of swarm control, in which swarms automatically adapt to changes in the world in the same way that a rat knows which detour to take around an unexpected obstacle. Unmanned robots are rapidly becoming a crucial technology for commercial, military, and scientific endeavors throughout the nation and across the globe. Critical future applications such as disaster relief and search & rescue will require intelligent spatial coordination among many robots spread over large geographical areas. This project will advance neural swarming as a control paradigm for this next generation of technological development. Additionally, this project will drive an extensive science, technology, engineering, and mathematics education program to bring the concepts of spatial intelligence, hippocampal information processing, and swarm control to high school students to improve literacy in neuroscience and robotics.

The project’s goal is to build a unified framework for self-organized, bottom-up control of spatial task planning that synergistically advances theoretical neuroscience and swarm control paradigms. In the project’s brain-to-swarm metaphor, neurons are autonomous agents, spikes are agent-based phase signals, and emergent circuit activity is emergent swarm behavior. The approach targets neural computations in hippocampal circuits and related systems that may contribute to online dynamic replanning. The research thrusts comprise data-driven dynamical network and point-process models of neural activity sequences, mathematical analysis of swarming dynamics using matrix manifolds, and autonomous systems simulations in realistic virtual environments. The project will advance understanding of emergent hippocampal dynamics and autonomous methods for dynamic replanning, motivating new research in distributed control. The project’s framework may enable mass-scalability for large, agile swarms of simple robotic agents.

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.

Reference link:

https://www.nsf.gov/awardsearch/showAward?AWD_ID=1835279&HistoricalAwards=false

https://hub.jhu.edu/2018/10/02/brain-robot-swarms-study/