Nonlinear control of nonholonomic mobile robot formations

In this thesis, the framework developed to control a single nonholonomic mobile robot is expanded to include the control of formations of multiple nonholonomic mobile robots. A combined kinematic/torque control law is developed for leader-follower based formation control using backstepping in order to accommodate the dynamics of the robots and the formation in contrast with kinematic-based formation controllers typically found in literature --Abstract, page iv

Control of Nonholonomic Mobile Robot Formations: Backstepping Kinematics into Dynamics

In this paper, we seek to expand framework developed to control a single nonholonomic mobile robot to include the control of formations of multiple nonholonomic mobile robots. A combined kinematic/torque control law is developed for leader-follower based formation control using backstepping in order to accommodate the dynamics of the robots and the formation in contrast with kinematic-based formation controllers. The asymptotic stability of the entire formation is guaranteed using Lyapunov theory, and numerical results are provided The kinematic controller is developed around control strategies for single mobile robots and the idea of virtual leaders. The virtual leader is replaced with a physical mobile robot leader and the assumption of constant reference velocities is removed An auxiliary velocity control is developed allowing the asymptotic stability of the followers to be proved without the use of Barbalat\u27s Lemma which simplifies proving the entire formation is asymptotically stable. A novel approach is taken in the development of the dynamical controller such that the torque control inputs for the follower robots include the dynamics of the follower robot as well as the dynamics of its leader, and the case when all robot dynamics are known is considered

Asymptotic Stability of Nonholonomic Mobile Robot Formations Using Multilayer Neural Networks

In this paper, a combined kinematic/torque control law is developed for leader-follower based formation control using backstepping in order to accommodate the dynamics of the robots and the formation in contrast with kinematic-based formation controllers that are widely reported in the literature. A multilayer neural network (NN) is introduced along with robust integral of the sign of the error (RISE) feedback to approximate the dynamics of the follower as well as its leader using online weight tuning. It is shown using Lyapunov theory that the errors for the entire formation are asymptotically stable and the NN weights are bounded as opposed to uniformly ultimately bounded (UUB) stability which is typical with most NN controllers. Simulation results are included

Control of Nonholonomic Mobile Robot Formations Using Neural Networks

In this paper the control of formations of multiple nonholonomic mobile robots is attempted by integrating a kinematic controller with a neural network (NN) computed-torque controller. A combined kinematic/torque control law is developed for leader-follower based formation control using backstepping in order to accommodate the dynamics of the robots and the formation in contrast with kinematic-based formation controllers. The NN is introduced to approximate the dynamics of the follower as well as its leader using online weight tuning. It is shown using Lyapunov theory that the errors for the entire formation are uniformly ultimately bounded, and numerical results are provided

Neural Network Control of Robot Formations Using RISE Feedback

In this paper, a combined kinematic/torque control law is developed for leader-follower based formation control using backstepping in order to accommodate the dynamics of the robots and the formation in contrast with kinematic-based formation controllers that are widely reported in the literature. A neural network (NN) is introduced along with robust integral of the sign of the error (RISE) feedback to approximate the dynamics of the follower as well as its leader using online weight tuning. It is shown using Lyapunov theory that the errors for the entire formation are asymptotically stable and the NN weights are bounded as opposed to uniformly ultimately bounded (UUB) stability which is typical with most NN controllers. Theoretical results are demonstrated using numerical simulations

Vision-based control of multi-agent systems

Scope and Methodology of Study: Creating systems with multiple autonomous vehicles places severe demands on the design of decision-making supervisors, cooperative control schemes, and communication strategies. In last years, several approaches have been developed in the literature. Most of them solve the vehicle coordination problem assuming some kind of communications between team members. However, communications make the group sensitive to failure and restrict the applicability of the controllers to teams of friendly robots. This dissertation deals with the problem of designing decentralized controllers that use just local sensor information to achieve some group goals.Findings and Conclusions: This dissertation presents a decentralized architecture for vision-based stabilization of unmanned vehicles moving in formation. The architecture consists of two main components: (i) a vision system, and (ii) vision-based control algorithms. The vision system is capable of recognizing and localizing robots. It is a model-based scheme composed of three main components: image acquisition and processing, robot identification, and pose estimation.Using vision information, we address the problem of stabilizing groups of mobile robots in leader- or two leader-follower formations. The strategies use relative pose between a robot and its designated leader or leaders to achieve formation objectives. Several leader-follower formation control algorithms, which ensure asymptotic coordinated motion, are described and compared. Lyapunov's stability theory-based analysis and numerical simulations in a realistic tridimensional environment show the stability properties of the control approaches

Formation control of mobile robots and unmanned aerial vehicles

In this dissertation, the nonlinear control of nonholonomic mobile robot formations and unmanned aerial vehicle (UAV) formations is undertaken and presented in six papers. In the first paper, an asymptotically stable combined kinematic/torque control law is developed for leader-follower based formation control of mobile robots using backstepping. A neural network (NN) is introduced along with robust integral of the sign of the error (RISE) feedback to approximate the dynamics of the follower as well as its leader using online weight tuning. Subsequently, in the second paper, a novel NN observer is designed to estimate the linear and angular velocities of both the follower and its leader robot and a NN output feedback control law is developed. On the other hand, in the third paper, a NN-based output feedback control law is presented for the control of an underactuated quad rotor UAV, and a NN virtual control input scheme is proposed which allows all six degrees of freedom to be controlled using only four control inputs. The results of this paper are extended to include the control of quadrotor UAV formations, and a novel three-dimensional leader-follower framework is proposed in the fourth paper. Next, in the fifth paper, the discrete-time nonlinear optimal control is undertaken using two online approximators (OLA\u27s) to solve the infinite horizon Hamilton-Jacobi-Bellman (HJB) equation forward-in-time to achieve nearly optimal regulation and tracking control. In contrast, paper six utilizes a single OLA to solve the infinite horizon HJB and Hamilton-Jacobi-Isaacs (HJI) equations forward-intime for the near optimal regulation and tracking control of continuous affine nonlinear systems. The effectiveness of the optimal tracking controllers proposed in the fifth and sixth papers are then demonstrated using nonholonomic mobile robot formation control --Abstract, page iv

Leader-Follower Control and Distributed Communication based UAV Swarm Navigation in GPS-Denied Environment

Unmanned Aerial Vehicles (UAVs) have developed rapidly in recent years due to technological advances and UAV technology finds applications in a wide range of fields, including surveillance, search and rescue, and agriculture. The utilization of UAV swarms in these contexts offers numerous advantages, increasing their value across different industries. These advantages include increased efficiency in tasks, enhanced productivity, greater safety, and the higher data quality. The coordination of UAVs becomes particularly crucial during missions in these applications, especially when drones are flying in close proximity as part of a swarm. For instance, if a drone swarm is targeted or needs to navigate through a Global Positioning System (GPS)-denied environment, it may encounter challenges in obtaining the location information typically provided by GPS. This poses a new challenge for the UAV swarms to maintain a reliable formation and successfully complete a given mission. In this article, our objective is to minimize the number of sensors required on each UAV and reduce the amount of information exchanged between UAVs. This approach aims to ensure the reliable maintenance of UAV formations with minimal communication requirements among UAVs while they follow predetermined trajectories during swarm missions. In this paper, we introduce a concept that utilizes extended Kalman filter, leader-follower-based control and a distributed data-sharing scheme to ensure the reliable and safe maintenance of formations and navigation autonomously for UAV swarm missions in GPS-denied environments. The formation control approaches and control strategies for UAV swarms are also discussed

Planning and estimation algorithms for human-like grasping

Mención Internacional en el título de doctorThe use of robots in human-like environments requires them to be able to sense and model unstructured scenarios. Thus, their success will depend on their versatility for interacting with the surroundings. This interaction often includes manipulation of objects for accomplishing common daily tasks. Therefore, robots need to sense, understand, plan and perform; and this has to be a continuous loop. This thesis presents a framework which covers most of the phases encountered in a common manipulation pipeline. First, it is shown how to use the Fast Marching Squared algorithm and a leader-followers strategy to control a formation of robots, simplifying a high dimensional path-planning problem. This approach is evaluated with simulations in complex environments in which the formation control technique is applied. Results are evaluated in terms of distance to obstacles (safety) and the needed deformation. Then, a framework to perform the grasping action is presented. The necessary techniques for environment modelling and grasp synthesis and path planning and control are presented. For the motion planning part, the formation concept from the previous chapter is recycled. This technique is applied to the planning and control of the movement of a complex hand-arm system. Tests using robot Manfred show the possibilities of the framework when performing in real scenarios. Finally, under the assumption that the grasping actions may not always result as it was previously planned, a Bayesian-based state-estimation process is introduced to estimate the final in-hand object pose after a grasping action is done, based on the measurements of proprioceptive and tactile sensors. This approach is evaluated in real experiments with Reex Takktile hand. Results show good performance in general terms, while suggest the need of a vision system for a more precise outcome.La investigación en robótica avanza con la intención de evolucionar hacia el uso de los robots en entornos humanos. A día de hoy, su uso está prácticamente limitado a las fábricas, donde trabajan en entornos controlados realizando tareas repetitivas. Sin embargo, estos robots son incapaces de reaccionar antes los más mínimos cambios en el entorno o en la tarea a realizar. En el grupo de investigación del Roboticslab se ha construido un manipulador móvil, llamado Manfred, en el transcurso de los últimos 15 años. Su objetivo es conseguir realizar tareas de navegación y manipulación en entornos diseñados para seres humanos. Para las tareas de manipulación y agarre, se ha adquirido recientemente una mano robótica diseñada en la universidad de Gifu, Japón. Sin embargo, al comienzo de esta tesis, no se había realzado ningún trabajo destinado a la manipulación o el agarre de objetos. Por lo tanto, existe una motivación clara para investigar en este campo y ampliar las capacidades del robot, aspectos tratados en esta tesis. La primera parte de la tesis muestra la aplicación de un sistema de control de formaciones de robots en 3 dimensiones. El sistema explicado utiliza un esquema de tipo líder-seguidores, y se basa en la utilización del algoritmo Fast Marching Square para el cálculo de la trayectoria del líder. Después, mientras el líder recorre el camino, la formación se va adaptando al entorno para evitar la colisión de los robots con los obstáculos. El esquema de deformación presentado se basa en la información sobre el entorno previamente calculada con Fast Marching Square. El algoritmo es probado a través de distintas simulaciones en escenarios complejos. Los resultados son analizados estudiando principalmente dos características: cantidad de deformación necesaria y seguridad de los caminos de los robots. Aunque los resultados son satisfactorios en ambos aspectos, es deseable que en un futuro se realicen simulaciones más realistas y, finalmente, se implemente el sistema en robots reales. El siguiente capítulo nace de la misma idea, el control de formaciones de robots. Este concepto es usado para modelar el sistema brazo-mano del robot Manfred. Al igual que en el caso de una formación de robots, el sistema al completo incluye un número muy elevado de grados de libertad que dificulta la planificación de trayectorias. Sin embargo, la adaptación del esquema de control de formaciones para el brazo-mano robótico nos permite reducir la complejidad a la hora de hacer la planificación de trayectorias. Al igual que antes, el sistema se basa en el uso de Fast Marching Square. Además, se ha construido un esquema completo que permite modelar el entorno, calcular posibles posiciones para el agarre, y planificar los movimientos para realizarlo. Todo ello ha sido implementado en el robot Manfred, realizando pruebas de agarre con objetos reales. Los resultados muestran el potencial del uso de este esquema de control, dejando lugar para mejoras, fundamentalmente en el apartado de la modelización de objetos y en el cálculo y elección de los posibles agarres. A continuación, se trata de cerrar el lazo de control en el agarre de objetos. Una vez un sistema robótico ha realizado los movimientos necesarios para obtener un agarre estable, la posición final del objeto dentro de la mano resulta, en la mayoría de las ocasiones, distinta de la que se había planificado. Este hecho es debido a la acumulación de fallos en los sistemas de percepción y modelado del entorno, y los de planificación y ejecución de movimientos. Por ello, se propone un sistema Bayesiano basado en un filtro de partículas que, teniendo en cuenta la posición de la palma y los dedos de la mano, los datos de sensores táctiles y la forma del objeto, estima la posición del objeto dentro de la mano. El sistema parte de una posición inicial conocida, y empieza a ejecutarse después del primer contacto entre los dedos y el objeto, de manera que sea capaz de detectar los movimientos que se producen al realizar la fuerza necesaria para estabilizar el agarre. Los resultados muestran la validez del método. Sin embargo, también queda claro que, usando únicamente la información táctil y de posición, hay grados de libertad que no se pueden determinar, por lo que, para el futuro, resultaría aconsejable la combinación de este sistema con otro basado en visión. Finalmente se incluyen 2 anexos que profundizan en la implementación de la solución del algoritmo de Fast Marching y la presentación de los sistemas robóticos reales que se han usado en las distintas pruebas de la tesis.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Carlos Balaguer Bernaldo de Quirós.- Secretario: Raúl Suárez Feijoo.- Vocal: Pedro U. Lim