1,058 research outputs found
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
Autonomous three-dimensional formation flight for a swarm of unmanned aerial vehicles
This paper investigates the development of a new guidance algorithm for a formation of unmanned aerial vehicles. Using the new approach of bifurcating potential fields, it is shown that a formation of unmanned aerial vehicles can be successfully controlled such that verifiable autonomous patterns are achieved, with a simple parameter switch allowing for transitions between patterns. The key contribution that this paper presents is in the development of a new bounded bifurcating potential field that avoids saturating the vehicle actuators, which is essential for real or safety-critical applications. To demonstrate this, a guidance and control method is developed, based on a six-degreeof-freedom linearized aircraft model, showing that, in simulation, three-dimensional formation flight for a swarm of unmanned aerial vehicles can be achieved
Connectivity-Preserving Swarm Teleoperation With A Tree Network
During swarm teleoperation, the human operator may threaten the
distance-dependent inter-robot communications and, with them, the connectivity
of the slave swarm. To prevent the harmful component of the human command from
disconnecting the swarm network, this paper develops a constructive strategy to
dynamically modulate the interconnections of, and the locally injected damping
at, all slave robots. By Lyapunov-based set invariance analysis, the explicit
law for updating that control gains has been rigorously proven to synchronize
the slave swarm while preserving all interaction links in the tree network. By
properly limiting the impact of the user command rather than rejecting it
entirely, the proposed control law enables the human operator to guide the
motion of the slave swarm to the extent to which it does not endanger the
connectivity of the swarm network. Experiment results demonstrate that the
proposed strategy can maintain the connectivity of the tree network during
swarm teleoperation
Multi-agent decision-making dynamics inspired by honeybees
When choosing between candidate nest sites, a honeybee swarm reliably chooses
the most valuable site and even when faced with the choice between near-equal
value sites, it makes highly efficient decisions. Value-sensitive
decision-making is enabled by a distributed social effort among the honeybees,
and it leads to decision-making dynamics of the swarm that are remarkably
robust to perturbation and adaptive to change. To explore and generalize these
features to other networks, we design distributed multi-agent network dynamics
that exhibit a pitchfork bifurcation, ubiquitous in biological models of
decision-making. Using tools of nonlinear dynamics we show how the designed
agent-based dynamics recover the high performing value-sensitive
decision-making of the honeybees and rigorously connect investigation of
mechanisms of animal group decision-making to systematic, bio-inspired control
of multi-agent network systems. We further present a distributed adaptive
bifurcation control law and prove how it enhances the network decision-making
performance beyond that observed in swarms
Safe, Remote-Access Swarm Robotics Research on the Robotarium
This paper describes the development of the Robotarium -- a remotely
accessible, multi-robot research facility. The impetus behind the Robotarium is
that multi-robot testbeds constitute an integral and essential part of the
multi-agent research cycle, yet they are expensive, complex, and time-consuming
to develop, operate, and maintain. These resource constraints, in turn, limit
access for large groups of researchers and students, which is what the
Robotarium is remedying by providing users with remote access to a
state-of-the-art multi-robot test facility. This paper details the design and
operation of the Robotarium as well as connects these to the particular
considerations one must take when making complex hardware remotely accessible.
In particular, safety must be built in already at the design phase without
overly constraining which coordinated control programs the users can upload and
execute, which calls for minimally invasive safety routines with provable
performance guarantees.Comment: 13 pages, 7 figures, 3 code samples, 72 reference
Quadcopter Attitude Control Optimization and Multi-Agent Coordination
This thesis presents a method of automated control gain tuning for a Quadcopter Unmanned Aerial Vehicle and proposes a method of coordination multiple autonomous robotic agents capable for formation aggregation.
Sliding Mode Control for Quadcopter altitude and attitude stabilization is presented and tuned using Particle Swarm Optimization. Different configurations for the optimization process are compared to determine an effective and time-efficient setup to complete the control gain tuning.
The multi-agent coordination scheme expands upon an existing adjustable swarm framework based on an Artificial Potential Field Sliding Mode Controller. The original leader-follower scheme is modified with the goal of producing a leaderless swarm where agents move towards specific locations to aggregate a desired formation. Analysis of the swarm control scheme pays particular attention to maintaining proper distance between agents. Using Lyapunov methods following that of the original controller analysis, stability under first order and general higher order dynamics is analyzed.
Numerical simulations of the swarm controller using agents with nonlinear Quadcopter or second order point mass dynamics are presented to illustrate the capabilities of this algorithm. The automatically tuned Quadcopter controller is used in simulations when applicable. The development of an experimental test platform is discussed with the intention of validating the simulation results on physical Quadcopters
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