COORDINATION OF LEADER-FOLLOWER MULTI-AGENT SYSTEM WITH TIME-VARYING OBJECTIVE FUNCTION

This thesis aims to introduce a new framework for the distributed control of multi-agent systems with adjustable swarm control objectives. Our goal is twofold: 1) to provide an overview to how time-varying objectives in the control of autonomous systems may be applied to the distributed control of multi-agent systems with variable autonomy level, and 2) to introduce a framework to incorporate the proposed concept to fundamental swarm behaviors such as aggregation and leader tracking. Leader-follower multi-agent systems are considered in this study, and a general form of time-dependent artificial potential function is proposed to describe the varying objectives of the system in the case of complete information exchange. Using Lyapunov methods, the stability and boundedness of the agents\u27 trajectories under single order and higher order dynamics are analyzed. Illustrative numerical simulations are presented to demonstrate the validity of our results. Then, we extend these results for multi-agent systems with limited information exchange and switching communication topology. The first steps of the realization of an experimental framework have been made with the ultimate goal of verifying the simulation results in practice

Integrated Robotic and Network Simulation Method

The increasing use of mobile cooperative robots in a variety of applications also implies an increasing research effort on cooperative strategies solutions, typically involving communications and control. For such research, simulation is a powerful tool to quickly test algorithms, allowing to do more exhaustive tests before implementation in a real application. However, the transition from an initial simulation environment to a real application may imply substantial rework if early implementation results do not match the ones obtained by simulation, meaning the simulation was not accurate enough. One way to improve accuracy is to incorporate network and control strategies in the same simulation and to use a systematic procedure to assess how different techniques perform. In this paper, we propose a set of procedures called Integrated Robotic and Network Simulation Method (IRoNS Method), which guide developers in building a simulation study for cooperative robots and communication networks applications. We exemplify the use of the improved methodology in a case-study of cooperative control comparison with and without message losses. This case is simulated with the OMNET++/INET framework, using a group of robots in a rendezvous task with topology control. The methodology led to more realistic simulations while improving the results presentation and analysis.info:eu-repo/semantics/publishedVersio

Reusable Software Components for Multi-Robot Foraging

Swarm intelligence is a rapidly growing area of robotics research that has the potential to reshape traditional approaches in many different fields, including military, agriculture, and medicine. However, a lack of widely available development platforms for swarm applications has hindered progress by forcing researchers to recreate previous efforts. The goal of this MQP is to provide a framework for developers to easily realize their own projects. The focus of this project is on identifying, programming, and evaluating the common behaviors that compose complex tasks such as foraging. The software components we developed can be easily reused and extended by other developers to realize other foraging algorithms

Reusable Software Components for Multi-Robot Foraging

Developments in swarm technologies is hindered by the lack of common libraries, which can lead to large amounts of repeated code and cause additional bugs in the program. The goal of this MQP is to provide such a framework for swarm developers to more easily create their own swarm projects. This project focuses on common, widely used behaviors in swarm research such as foraging, which seeks to mimic how groups of ants or bees find and retrieve food in nature. The team will identify, program, and evaluate the core behaviors common to foraging algorithms. Based on this research, a library of swarm behaviors will be developed that allows future developers to perform foraging and other swarm-centric tasks easily