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- A bottom-up approach to programming swarms of robots can be achieved by taking inspiration from complexity science, allowing the robots to interact with one another and learn and adapt their behaviors based on the environment.
- The combination of leveraging existing biological principles, such as copying searching behaviors observed in ants, and applying mathematical scaling laws enables successful programming and scaling of robot swarms.
Deep dives
Programming swarms of robots using complexity science
In this podcast episode, Melanie Moses, a computer science professor, discusses how a bottom-up approach to programming swarms of robots can be achieved by taking inspiration from complexity science. By observing the behavior of foraging ants, Melanie and her team built a swarm of robots called IANTS that could collectively identify and gather resources. Using evolutionary algorithms, the robots were able to learn and adapt their behaviors based on the environment, such as when to return to a location, when to communicate with other robots, and how to efficiently collect resources. This approach demonstrated the possibility of engineering emergent properties in robot swarms and achieving cooperation to accomplish a common goal.
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