Connectivity Preservation in Multi-Agent Systems using Model Predictive Control

Flocking of multiagent systems is one of the basic behaviors in the field of control of multiagent systems and it is an essential element of many real-life applications. Such systems under various network structures and environment modes have been extensively studied in the past decades. Navigation of agents in a leader-follower structure while operating in environments with obstacles is particularly challenging. One of the main challenges in flocking of multiagent systems is to preserve connectivity. Gradient descent method is widely utilized to achieve this goal. But the main shortcoming of applying this method for the leader-follower structure is the need for continuous data transmission between agents and/or the preservation of a fixed connection topology. In this research, we propose an innovative model predictive controller based on a potential field that maintains the connectivity of a flock of agents in a leader-follower structure with dynamic topology. The agents navigate through an environment with obstacles that form a path leading to a certain target. Such a control technique avoids collisions of followers with each other without using any communication links while following their leader which navigates in the environment through potential functions for modelling the neighbors and obstacles. The potential field is dynamically updated by introducing weight variables in order to preserve connectivity among the followers as we assume only the leader knows the target position. The values of these weights are changed in real-time according to trajectories of the agents when the critical neighbors of each agent is determined. We compare the performance of our predictive-control based algorithm with other approaches. The results show that our algorithm causes the agents to reach the target in less time. However, our algorithm faces more deadlock cases when the agents go through relatively narrow paths. Due to the consideration of the input costs in our controller, the group of agents reaching the target faster does not necessarily result in the followers consuming more energy than the leader

Safe Connectivity Maintenance in Underactuated Multi-Agent Networks for Dynamic Oceanic Environments

Autonomous Multi-Agent Systems are increasingly being deployed in environments where winds and ocean currents can exert a significant influence on their dynamics. Recent work has developed powerful control policies for single agents that can leverage flows to achieve their objectives in dynamic environments. However, in the context of multi-agent systems, these flows can cause agents to collide or drift apart and lose direct inter-agent communications, especially when agents have low propulsion capabilities. To address these challenges, we propose a Hierarchical Multi-Agent Control approach that allows arbitrary single agent performance policies that are unaware of other agents to be used in multi-agent systems, while ensuring safe operation. We first develop a safety controller solely dedicated to avoiding collisions and maintaining inter-agent communication. Subsequently, we design a low-interference safe interaction (LISIC) policy that trades-off the performance policy and the safety controller to ensure safe and optimal operation. Specifically, when the agents are at an appropriate distance, LISIC prioritizes the performance policy, while smoothly increasing the safety controller when necessary. We prove that under mild assumptions on the flows experienced by the agents our approach can guarantee safety. Additionally, we demonstrate the effectiveness of our method in realistic settings through an extensive empirical analysis with underactuated Autonomous Surface Vehicles (ASV) operating in dynamical ocean currents where the assumptions do not always hold.Comment: 8 pages, submitted to 2023 IEEE 62th Annual Conference on Decision and Control (CDC) Nicolas Hoischen and Marius Wiggert contributed equally to this wor

An Overview of Recent Progress in the Study of Distributed Multi-agent Coordination

This article reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, task assignment, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations

Modeling and Control of Multi-Agent Systems

Biology has brought much enlightenment to the development of human technology, for example, the collective behaviors inspired engineering applications (such as, the unmanned vehicle formation, the satellite alignment etc.), and even the study of network theory. This discipline has made a significant contribution to technology development. As a prospective solution to the current issues, multi-agent control has become a popular research topic in recent decades. The traditional control methods based on the classical models are suffering from high sensitivity to model accuracy, computational complexity, low fault tolerance, and weakness in real-time performance. Therefore, the advantages of multi-agent control are obvious: 1) easy maintenance and expansion of the system by repairing, replacing or adding agents; 2) high fault tolerance and robustness, ability to function properly even when some agents fail; 3) low requirement of distributed controllers, which brings low cost and large flexibility. In this thesis, I investigate problems on modeling and control of multi-agent systems. In particular, I propose a three-dimensional model to simulate collective behavior under high-speed conditions. I design an improved adaptive-velocity strategy and weighted strategy to enhance the performance of the multi-agent system. Moreover, I analyze the performance from the aspects of energy and parameter space. I show how the model works and its advantages compared to existing models. Then, I study the design of distributed controllers for multi-agent systems. Output regulation with input saturation and nonlinear flocking problems are studied with the assumption of a heterogeneous switching topology. The output regulation problem is solved via low gain state feedback and its validity verified by theoretical study. Then, the flocking problem with heterogeneous nonlinear dynamics is solved. A connectivity-preserving algorithm and potential function are designed to ensure the controllability of the multi-agent system through the dynamic process. Overall, this thesis provides examples of how to analyze and manipulate multi-agent systems. It offers promising solutions to solve physical multi-agent modeling and control problems and provides ideas for bio-inspired engineering and artificial intelligent control for multi-agent systems

Adaptive Synchronization of Complex Dynamical Networks with State Predictor

This paper addresses the adaptive synchronization of complex dynamical networks with nonlinear dynamics. Based on the Lyapunov method, it is shown that the network can synchronize to the synchronous state by introducing local adaptive strategy to the coupling strengths. Moreover, it is also proved that the convergence speed of complex dynamical networks can be increased via designing a state predictor. Finally, some numerical simulations are worked out to illustrate the analytical results

Ultrafast Consensus in Small-World Networks

In this paper, we demonstrate a phase transition phenomenon in algebraic connectivity of small-world networks. Algebraic connectivity of a graph is the second smallest eigenvalue of its Laplacian matrix and a measure of speed of solving consensus problems in networks. We demonstrate that it is possible to dramatically increase the algebraic connectivity of a regular complex network by 1000 times or more without adding new links or nodes to the network. This implies that a consensus problem can be solved incredibly fast on certain small-world networks giving rise to a network design algorithm for ultra fast information networks. Our study relies on a procedure called "random rewiring" due to Watts & Strogatz (Nature, 1998). Extensive numerical results are provided to support our claims and conjectures. We prove that the mean of the bulk Laplacian spectrum of a complex network remains invariant under random rewiring. The same property only asymptotically holds for scale-free networks. A relationship between increasing the algebraic connectivity of complex networks and robustness to link and node failures is also shown. This is an alternative approach to the use of percolation theory for analysis of network robustness. We also show some connections between our conjectures and certain open problems in the theory of random matrices

Semiglobal observer-based leader- following consensus with input saturation

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Imitation Learning for Swarm Control using Variational Inference

Swarms are groups of robots that can coordinate, cooperate, and communicate to achieve tasks that may be impossible for a single robot. These systems exhibit complex dynamical behavior, similar to those observed in physics, neuroscience, finance, biology, social and communication networks, etc. For instance, in Biology, schools of fish, swarm of bacteria, colony of termites exhibit flocking behavior to achieve simple and complex tasks. Modeling the dynamics of flocking in animals is challenging as we usually do not have full knowledge of the dynamics of the system and how individual agent interact. The environment of swarms is also very noisy and chaotic. We usually only can observe the individual trajectories of the agents. This work presents a technique to learn how to discover and understand the underlying governing dynamics of these systems and how they interact from observation data alone using variational inference in an unsupervised manner. This is done by modeling the observed system dynamics as graphs and reconstructing the dynamics using variational autoencoders through multiple message passing operations in the encoder and decoder. By achieving this, we can apply our understanding of the complex behavior of swarm of animals to robotic systems to imitate flocking behavior of animals and perform decentralized control of robotic swarms. The approach relies on data-driven model discovery to learn local decentralized controllers that mimic the motion constraints and policies of animal flocks. To verify and validate this technique, experiments were done on observations from schools of fish and synthetic data from boids model