Spiking Neural Networks as a Controller for Emergent Swarm Agents
Drones which can swarm and loiter in a certain area cost hundreds of dollars, but mosquitos can do the same and are essentially worthless. To control swarms of low-cost robots, researchers may end up spending countless hours brainstorming robot configurations and policies to "organically"...
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| Vydáno v: | 2024 International Conference on Neuromorphic Systems (ICONS) s. 319 - 326 |
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IEEE
30.07.2024
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| Abstract | Drones which can swarm and loiter in a certain area cost hundreds of dollars, but mosquitos can do the same and are essentially worthless. To control swarms of low-cost robots, researchers may end up spending countless hours brainstorming robot configurations and policies to "organically" create behaviors which do not need expensive sensors and perception. Existing research explores the possible emergent behaviors in swarms of robots with only a binary sensor and a simple but hand-picked controller structure. Even agents in this highly limited sensing, actuation, and computational capability class can exhibit relatively complex global behaviors such as aggregation, milling, and dispersal, but finding the local interaction rules that enable more collective behaviors remains a significant challenge. This paper investigates the feasibility of training spiking neural networks to find those local interaction rules that result in particular emergent behaviors. In this paper, we focus on simulating a specific milling behavior already known to be producible using very simple binary sensing and acting agents. To do this, we use evolutionary algorithms to evolve not only the parameters (the weights, biases, and delays) of a spiking neural network, but also its structure. To create a baseline, we also show an evolutionary search strategy over the parameters for the incumbent hand-picked binary controller structure. Our simulations show that spiking neural networks can be evolved in binary sensing agents to form a mill. |
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| AbstractList | Drones which can swarm and loiter in a certain area cost hundreds of dollars, but mosquitos can do the same and are essentially worthless. To control swarms of low-cost robots, researchers may end up spending countless hours brainstorming robot configurations and policies to "organically" create behaviors which do not need expensive sensors and perception. Existing research explores the possible emergent behaviors in swarms of robots with only a binary sensor and a simple but hand-picked controller structure. Even agents in this highly limited sensing, actuation, and computational capability class can exhibit relatively complex global behaviors such as aggregation, milling, and dispersal, but finding the local interaction rules that enable more collective behaviors remains a significant challenge. This paper investigates the feasibility of training spiking neural networks to find those local interaction rules that result in particular emergent behaviors. In this paper, we focus on simulating a specific milling behavior already known to be producible using very simple binary sensing and acting agents. To do this, we use evolutionary algorithms to evolve not only the parameters (the weights, biases, and delays) of a spiking neural network, but also its structure. To create a baseline, we also show an evolutionary search strategy over the parameters for the incumbent hand-picked binary controller structure. Our simulations show that spiking neural networks can be evolved in binary sensing agents to form a mill. |
| Author | Zhu, Kevin Mattson, Connor Nowzari, Cameron Parsa, Maryam Vega, Ricardo Brown, Daniel S. Snyder, Shay |
| Author_xml | – sequence: 1 givenname: Kevin surname: Zhu fullname: Zhu, Kevin email: kzhu4@gmu.edu organization: George Mason University,Fairfax,USA – sequence: 2 givenname: Connor surname: Mattson fullname: Mattson, Connor email: c.mattson@utah.edu organization: University of Utah,Salt Lake City,USA – sequence: 3 givenname: Shay surname: Snyder fullname: Snyder, Shay email: ssnyde9@gmu.edu organization: George Mason University,Fairfax,USA – sequence: 4 givenname: Ricardo surname: Vega fullname: Vega, Ricardo email: rvega7@gmu.edu organization: George Mason University,Fairfax,USA – sequence: 5 givenname: Daniel S. surname: Brown fullname: Brown, Daniel S. email: daniel.s.brown@utah.edu organization: University of Utah,Salt Lake City,USA – sequence: 6 givenname: Maryam surname: Parsa fullname: Parsa, Maryam email: mparsa@gmu.edu organization: George Mason University,Fairfax,USA – sequence: 7 givenname: Cameron surname: Nowzari fullname: Nowzari, Cameron email: cnowzari@gmu.edu organization: George Mason University,Fairfax,USA |
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| Snippet | Drones which can swarm and loiter in a certain area cost hundreds of dollars, but mosquitos can do the same and are essentially worthless. To control swarms of... |
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| SubjectTerms | Evolutionary algorithms Heuristic algorithms Measurement Milling Neuromorphics Particle swarm optimization Robot sensing systems Search problems Sensors Spiking neural networks Swarming Behaviors Training |
| Title | Spiking Neural Networks as a Controller for Emergent Swarm Agents |
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