Multi-Agent Sim with Realistic Comms
BotNet
In the fall and winter of 2020-21, I built and tested a simulation platform using the 6TiSCH wireless mesh network simulator as a pluggable interface to any multi-agent robotic simulator, allowing development and testing of decentralized multi-agent control with various RF propagation & wireless communication models.
Leaving to intern at Plato Systems in early 2021, I handed off the simulator and de-centralized multi-agent control experiments to the multi-agent research group at Swarm Lab and continued supporting development and experiments through paper submission to IEEE MRS 2021 [1], where the paper was a finalist for Best Student Paper Award.
Advisor: Professor Kristofer Pister
Collaborators: Mark Selden, Jason Zhou, Nathan Lambert PhD (Hugging Face), Professor Daniel Drew (UofU ECE)
Publications
1. Mark Selden, Jason Zhou, Felipe M. R. Campos, Nathan Lambert, Daniel Drew, K. S. J. Pister. “BotNet: A Simulator for Studying the Effects of Accurate Communication Models on Multi-Agent and Swarm Control.” IEEE International Symposium on Multi-Robot and Multi-Agent Systems. Cambridge, UK: IEEE, Oct. 2021 (link)
Decentralized control in multi-robot systems is dependent on accurate and reliable communication between agents. Important communication factors, such as latency and packet delivery ratio, are strong functions of the number of agents in the network. Findings from studies of mobile and high node-count radio-frequency (RF) mesh networks have only been transferred to the domain of multi-robot systems to a limited extent, and typical multi-agent robotic simulators often depend on simple propagation models that do not reflect the behavior of realistic RF networks. In this paper, we present a new open source swarm robotics simulator, BotNet, with an embedded standards-compliant time-synchronized channel hopping (6TiSCH) RF mesh network simulator. Using this simulator we show how more accurate communications models can limit even simple multi-robot control tasks such as flocking and formation control, with agent counts ranging from 10 up to 2500 agents. The experimental results are used to motivate changes to the inter-robot communication propagation models and other networking components currently used in practice in order to bridge the sim-to-real gap.
