Gesture based Human-Swarm Interactions for Formation Control using interpreters
This addresses the challenge of intuitive human-swarm interaction for formation control, though it appears incremental by combining existing techniques like machine learning and optimal control.
The paper tackles the problem of controlling swarm formations using human gestures, proposing a framework that interprets arm gestures via an intermediary system to generate swarm commands, and demonstrates real-time control of simulated robots in 2D.
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.