Human-Swarm Interactions. Learning of Swarm Dynamics using Gaussian Processes

Inhaltsverzeichnis (Table of Contents)

• Introduction
  • Human-Swarm Interactions
  • Gaussian Process Regression
  • State of the Art
• Swarm Dynamics
  • Graph Theory
  • Swarm Dynamics and Consensus
  • System Analysis
• Learning of Swarm Dynamics using Gaussian Process Regression
  • Basics of Gaussian Process Regression
  • Gaussian Process Regression for a Linear Multi-Agent System
  • Gaussian Process Regression for a Nonlinear Multi-Agent System
  • Prediction of the Future Position and Application
  • Experimental Verification
• Summary and Outlook
  • Summary
  • Outlook

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This thesis aims to explore the application of Gaussian Process Regression (GPR) for learning the dynamics of a swarm robot system. The goal is to develop a model capable of predicting future states of the swarm, enabling human operators to control the agents towards a desired destination.

• Modeling swarm dynamics using GPR
• Influence of training data on model accuracy
• Prediction of future swarm positions
• Human-swarm interaction and control
• Experimental verification of the learned model

Zusammenfassung der Kapitel (Chapter Summaries)

• Introduction: This chapter introduces the concept of human-swarm interaction, highlighting the need for accurate models to predict swarm behavior. It also provides background information on Gaussian Process Regression and discusses relevant research in the field.
• Swarm Dynamics: This chapter delves into the fundamental concepts of swarm dynamics, including graph theory, consensus algorithms, and system analysis. It lays the groundwork for understanding the complexities of multi-agent systems.
• Learning of Swarm Dynamics using Gaussian Process Regression: This chapter focuses on the application of GPR for learning swarm dynamics. It discusses the basics of GPR, its implementation for both linear and nonlinear systems, and the use of the learned model for predicting future positions.

Schlüsselwörter (Keywords)

The key keywords and focus topics of this thesis include swarm robotics, Gaussian process regression, machine learning, human-swarm interaction, multi-agent systems, system dynamics, and consensus algorithms.