Multi Agent Framework for Collective Intelligence Research

TL;DR

This paper introduces a scalable multi-agent framework for collective intelligence research, enabling diverse experiments from message exchange to real drone coordination, with a focus on synthetic perception and sim-to-real transfer.

Contribution

It presents a novel decentralized multi-agent framework with synthetic perception maps and real drone experiments, bridging simulation and real-world UAV coordination.

Findings

UAV Crazyflie drones successfully followed collision-free trajectories.

The framework supports diverse collective intelligence experiments.

Synthetic perception maps effectively guide agent behavior.

Abstract

This paper presents a scalable decentralized multi agent framework that facilitates the exchange of information between computing units through computer networks. The architectural boundaries imposed by the tool make it suitable for collective intelligence research experiments ranging from agents that exchange hello world messages to virtual drone agents exchanging positions and eventually agents exchanging information via radio with real Crazyflie drones in VU Amsterdam laboratory. The field modulation theory is implemented to construct synthetic local perception maps for agents, which are constructed based on neighbouring agents positions and neighbouring points of interest dictated by the environment. By constraining the experimental setup to a 2D environment with discrete actions, constant velocity and parameters tailored to VU Amsterdam laboratory, UAV Crazyflie drones running hill climbing controller followed collision-free trajectories and bridged sim-to-real gap.