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

Swarm Metaverse for Multi-Level Autonomy Using Digital Twins

Robot swarms are becoming popular in domains that require spatial coordination. Effective human control over swarm members is pivotal for ensuring swarm behaviours align with the dynamic needs of the system. Several techniques have been proposed for scalable human–swarm interaction. However, these techniques were mostly developed in simple simulation environments without guidance on how to scale them up to the real world. This paper addresses this research gap by proposing a metaverse for scalable control of robot swarms and an adaptive framework for different levels of autonomy. In the metaverse, the physical/real world of a swarm symbiotically blends with a virtual world formed from digital twins representing each swarm member and logical control agents. The proposed metaverse drastically decreases swarm control complexity due to human reliance on only a few virtual agents, with each agent dynamically actuating on a sub-swarm. The utility of the metaverse is demonstrated by a case study where humans controlled a swarm of uncrewed ground vehicles (UGVs) using gestural communication, and via a single virtual uncrewed aerial vehicle (UAV). The results show that humans could successfully control the swarm under two different levels of autonomy, while task performance increases as autonomy increases.</p

Scale-Invariant Specifications for Human-Swarm Systems

We present a method for controlling a swarm using its spectral decomposition -- that is, by describing the set of trajectories of a swarm in terms of a spatial distribution throughout the operational domain -- guaranteeing scale invariance with respect to the number of agents both for computation and for the operator tasked with controlling the swarm. We use ergodic control, decentralized across the network, for implementation. In the DARPA OFFSET program field setting, we test this interface design for the operator using the STOMP interface -- the same interface used by Raytheon BBN throughout the duration of the OFFSET program. In these tests, we demonstrate that our approach is scale-invariant -- the user specification does not depend on the number of agents; it is persistent -- the specification remains active until the user specifies a new command; and it is real-time -- the user can interact with and interrupt the swarm at any time. Moreover, we show that the spectral/ergodic specification of swarm behavior degrades gracefully as the number of agents goes down, enabling the operator to maintain the same approach as agents become disabled or are added to the network. We demonstrate the scale-invariance and dynamic response of our system in a field relevant simulator on a variety of tactical scenarios with up to 50 agents. We also demonstrate the dynamic response of our system in the field with a smaller team of agents. Lastly, we make the code for our system available.Comment: Journal of Field Robotics, Accepted for Publication. 25 page

Human Interaction with Robot Swarms: A Survey

Recent advances in technology are delivering robots of reduced size and cost. A natural outgrowth of these advances are systems comprised of large numbers of robots that collaborate autonomously in diverse applications. Research on effective autonomous control of such systems, commonly called swarms, has increased dramatically in recent years and received attention from many domains, such as bioinspired robotics and control theory. These kinds of distributed systems present novel challenges for the effective integration of human supervisors, operators, and teammates that are only beginning to be addressed. This paper is the first survey of human–swarm interaction (HSI) and identifies the core concepts needed to design a human–swarm system. We first present the basics of swarm robotics. Then, we introduce HSI from the perspective of a human operator by discussing the cognitive complexity of solving tasks with swarm systems. Next, we introduce the interface between swarm and operator and identify challenges and solutions relating to human–swarm communication, state estimation and visualization, and human control of swarms. For the latter, we develop a taxonomy of control methods that enable operators to control swarms effectively. Finally, we synthesize the results to highlight remaining challenges, unanswered questions, and open problems for HSI, as well as how to address them in future works

Human Control of Leader-Based Swarms

As swarms are used in increasingly more complex scenarios, further investigation is needed to determine how to give human operators the best tools to properly influence the swarm after deployment. Previous research has focused on relaying influence from the operator to the swarm, either by broadcasting commands to the entire swarm or by influencing the swarm through the teleoperation of a leader. While these methods each have their different applications, there has been a lack of research into how the influence should be propagated through the swarm in leader-based methods. This paper focuses on two simple methods of information propagation-flooding and consensus-and compares the ability of operators to maneuver the swarm to goal points using each, both with and without sensing error. Flooding involves each robot explicitly matching the speed and direction of the leader (or matching the speed and direction of the first neighboring robot that has already done so), and consensus involves each robot matching the average speed and direction of all the neighbors it senses. We discover that the flooding method is significantly more effective, yet the consensus method has some advantages at lower speeds, and in terms of overall connectivity and cohesion of the swarm