Resilient and Decentralized Control of Multi-level Cooperative Mobile Networks to Maintain Connectivity under Adversarial Environment

Network connectivity plays an important role in the information exchange between different agents in the multi-level networks. In this paper, we establish a game-theoretic framework to capture the uncoordinated nature of the decision-making at different layers of the multi-level networks. Specifically, we design a decentralized algorithm that aims to maximize the algebraic connectivity of the global network iteratively. In addition, we show that the designed algorithm converges to a Nash equilibrium asymptotically and yields an equilibrium network. To study the network resiliency, we introduce three adversarial attack models and characterize their worst-case impacts on the network performance. Case studies based on a two-layer mobile robotic network are used to corroborate the effectiveness and resiliency of the proposed algorithm and show the interdependency between different layers of the network during the recovery processes.Comment: 9 pages, 6 figure

A differential game approach to urban drainage systems control

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Urban drainage systems (UDSs) are complex large-scale systems that carry stormwater and wastewater throughout urban areas. During heavy rain scenarios, UDSs are not able to handle the amount of extra water that enters the network and flooding occurs. Usually, this might happen because the network is not being used efficiently, i.e., some structures remain underused while many others are overused. This paper proposes a control methology based on differential game theory that aims to efficiently use the existing network elements in order to minimize overflows and properly manage the water resource. The proposed controller is tested on a typical UDS and is compared with a centralized MPC achieving similar results in terms of flooding minimization and network usage, but only using local information on distributed controllers.Peer ReviewedPostprint (author's final draft

Comprehensive review on controller for leader-follower robotic system

985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies

Decentralized Multi-Robot Social Navigation in Constrained Environments via Game-Theoretic Control Barrier Functions

We present an approach to ensure safe and deadlock-free navigation for decentralized multi-robot systems operating in constrained environments, including doorways and intersections. Although many solutions have been proposed to ensure safety, preventing deadlocks in a decentralized fashion with global consensus remains an open problem. We first formalize the objective as a non-cooperative, non-communicative, partially observable multi-robot navigation problem in constrained spaces with multiple conflicting agents, which we term as social mini-games. Our approach to ensuring safety and liveness rests on two novel insights: (i) deadlock resolution is equivalent to deriving a mixed-Nash equilibrium solution to a social mini-game and (ii) this mixed-Nash strategy can be interpreted as an analogue to control barrier functions (CBFs), that can then be integrated with standard CBFs, inheriting their safety guarantees. Together, the standard CBF along with the mixed-Nash CBF analogue preserves both safety and liveness. We evaluate our proposed game-theoretic navigation algorithm in simulation as well on physical robots using F1/10 robots, a Clearpath Jackal, as well as a Boston Dynamics Spot in a doorway, corridor intersection, roundabout, and hallway scenario. We show that (i) our approach results in safer and more efficient navigation compared to local planners based on geometrical constraints, optimization, multi-agent reinforcement learning, and auctions, (ii) our deadlock resolution strategy is the smoothest in terms of smallest average change in velocity and path deviation, and most efficient in terms of makespan (iii) our approach yields a flow rate of 2.8 - 3.3 (ms)^{-1 which is comparable to flow rate in human navigation at 4 (ms)^{-1}.Comment: arXiv admin note: text overlap with arXiv:2306.0881

Control multiagente de vehículos autónomos en presencia de agentes no cooperativos utilizando teoría de juegos

ilustraciones, fotografías a colorThis thesis proposes a solution to the problem of autonomous vehicle driving in a road environment, specifically in the presence of agent-driven vehicles with selfish decisions and aggressive maneuvers. The controller tries to solve the optimization problem using a \textit{Model Predictive Control}(MPC). Taking advantage of the previous technique for trajectory prediction, the controller uses this to better predict the neighbors' position and plan its trajectory. In addition, the predictive model can solve the \textit{Optimal Control Problem} by complying with security restrictions, avoiding obstacles, and achieving its primary objective. The \textit{Optimal Control Problem} has non-convex constraints due to its based on mixed-integer variables. By creating non-linear MPC that can deal with the problem of hybrid variables, it is sought to solve the problem of driving vehicles against aggressive and non-cooperative decisions for the network. Furthermore, all agents in the system can be controlled by creating local controllers based on \textit{Game Theory}. We analyzed two methods to find an optimal solution: centralized and decentralized. The most effective and viable controller is chosen after objective research and comparison of all others. Since the centralized MPC provides the best solution for the entire plant, it is used as a benchmark. The first decomposed algorithm is centralized MPC, in which the neighboring subsystems give the information to the central node, calculate the new routes and transmit in each iteration of the MPC. The second approach is based on optimal distributed decentralized MPC. The cars are based on the \textit{Generalized Potential Game theory} in both cases. Each agent solves its problem sequentially and shares its next move with neighbors, looking for a $\epsilon$-Nash equilibrium. Both drivers can feasibly calculate their trajectory by relying on additional constraints while avoiding other vehicles. Distributed controllers are evaluated in three different scenarios, using three criteria: the efficiency of the global controller, the time it takes for each controller to find an answer, and the feasibility of the controller with the increase in steps that the controller must predict. The first scenario gives an idea of the controller's behavior against agents with unknown maneuvers; the second shows the controller's behavior against increased constraints and connections with neighbors, and the third tests the controller by reducing its environmental variables. (Texto tomado de la fuente)Esta tesis propone una solución al problema de la conducción autónoma de vehículos en un entorno vial, concretamente en presencia de vehículos conducidos por agentes con decisiones egoístas y maniobras agresivas. El controlador trata de resolver el problema de optimización basado en un Control Predictivo de Modelo (MPC). Aprovechando la técnica anterior de predicción de trayectoria, el controlador la utiliza para predecir mejor la posición de los vecinos y planificar su trayectoria. Además, el modelo predictivo puede resolver el problema de control óptimo al cumplir con las restricciones de seguridad, evitar obstáculos y lograr su objetivo principal. El problema de control óptimo tiene restricciones no convexas debido a las variables enteras mixtas en las que se basa. Mediante la creación de MPC no lineales que puedan lidiar con el problema de las variables híbridas, se busca resolver el problema de conducción de vehículos frente a decisiones agresivas y no cooperativas para la red. Además, todos los agentes del sistema pueden controlarse mediante la creación de controladores locales basados en \textit{Teoría de Juegos}. Analizamos dos métodos para encontrar una solución óptima: centralizado y descentralizado. El controlador más eficaz y viable se elige después de una investigación objetiva y la comparación de todos los demás. Dado que el MPC proporciona la mejor solución para toda la planta, se utiliza como punto de referencia. El primer algoritmo descompuesto es MPC centralizado, en el que los subsistemas vecinos entregan la información al nodo central, calculan las nuevas rutas y transmiten en cada iteración del controlador por MPC. El segundo enfoque se basa en MPC descentralizado distribuido óptimo. Los coches se basan en la teoría del Juego de Potencial Generalizado en ambos casos. Cada agente resuelve su problema secuencialmente y comparte su próximo movimiento con los vecinos, buscando un equilibrio $\epsilon$-Nash. Ambos conductores pueden calcular su trayectoria de manera factible confiando en restricciones adicionales mientras evitan otros vehículos. Los controladores distribuidos se evalúan en tres escenarios diferentes, utilizando tres criterios: la eficiencia del controlador global, el tiempo que tarda cada controlador en encontrar una respuesta y la viabilidad del controlador con el aumento de pasos que el controlador debe predecir. El primer escenario da una idea del comportamiento del controlador frente a agentes con maniobras desconocidas; el segundo muestra el comportamiento del controlador frente a mayores restricciones y conexiones con vecinos, y el tercero prueba el controlador reduciendo sus variables ambientales.MaestríaMagíster en Ingeniería - Automatización IndustrialControlRobotic