Gesture based Human-Swarm Interactions for Formation Control using interpreters

TL;DR

This paper introduces a gesture-based human-swarm interaction framework that uses wearable armbands and machine learning to translate user gestures into swarm control commands, enabling real-time formation control in 2D environments.

Contribution

It presents a novel interpreter system combining machine learning and control techniques to simplify human-swarm interaction and introduces a decentralized formation controller for swarm management.

Findings

Successfully demonstrated real-time control of a simulated robot swarm.

Validated the framework through theoretical analysis and experimental results.

Showcased effective gesture-based control in a 2D environment.

Abstract

We propose a novel Human-Swarm Interaction (HSI) framework which enables the user to control a swarm shape and formation. The user commands the swarm utilizing just arm gestures and motions which are recorded by an off-the-shelf wearable armband. We propose a novel interpreter system, which acts as an intermediary between the user and the swarm to simplify the user's role in the interaction. The interpreter takes in a high level input drawn using gestures by the user, and translates it into low level swarm control commands. This interpreter employs machine learning, Kalman filtering and optimal control techniques to translate the user input into swarm control parameters. A notion of Human Interpretable dynamics is introduced, which is used by the interpreter for planning as well as to provide feedback to the user. The dynamics of the swarm are controlled using a novel decentralized formation controller based on distributed linear iterations and dynamic average consensus. The framework is demonstrated theoretically as well as experimentally in a 2D environment, with a human controlling a swarm of simulated robots in real time.