Abstract: Robots in a swarm are programmed with individual behaviors but then interactions with the environment and other robots produce more complex, emergent swarm behaviors. A partial differential equation (PDE) can be used to accurately quantify the distribution of robots throughout the environment at any given time if the robots have simple individual behaviors and there are a finite number of potential environments. A least mean square algorithm can then be used to compare a given observation of the swarm distribution to the potential models to accurately identify the environment being explored. This technique affirms that there is a correlation between the individual robot behaviors, robot distribution, and the environment being explored. For more complex behaviors and environments, there is no closed-form model for the emergent behavior but there is still a correlation which can be used to infer one property if the other two are known. A simple, single-layer neural network can replace the PDE and be trained to correlate local observations of the robot distribution to the environment being explored. The neural network approach allows for more sophisticated robot behaviors, more varied environments, and is robust to variations in environment type and number of robots. This novel approach lays a foundation for using minimalist swarms, where robots have simple motions and no communication, to achieve collective sensing.

Adviser: Dr. Anthony Maciejewski
Co-Adviser: Dr. Edwin Chong
Non-ECE Member: Dr. Chuck Anderson, CS
Member 3: Dr. Peter Young, ECE
Addional Members: N/A

Publications:
M. Emmons, A. A. Maciejewski, E. K. P. Chong, "Modelling Emergent Swarm Behavior Using Continuum Limits for Environmental Mapping", Proc. IEEE International Conference on Control and Automation, June 2018, pgs. 86-93

M. Emmons, A. A. Maciejewski, C. Anderson, E. K. P. Chong, "Classifying Environmental Features from Local Observations of Emergent Swarm Behavior", Journal of Automatica Sinica, 2020

Program of Study:
ECE656
ECE514
ECE545
ECE612
ECE555
ECE520
ECE666
N/A