Virtual Structure Based Formation Tracking of Multiple Wheeled Mobile Robots: An Optimization Perspective

Today, with the increasing development of science and technology, many systems need to be optimized to find the optimal solution of the system. this kind of problem is also called optimization problem. Especially in the formation problem of multi-wheeled mobile robots, the optimization algorithm can help us to find the optimal solution of the formation problem. In this paper, the formation problem of multi-wheeled mobile robots is studied from the point of view of optimization. In order to reduce the complexity of the formation problem, we first put the robots with the same requirements into a group. Then, by using the virtual structure method, the formation problem is reduced to a virtual WMR trajectory tracking problem with placeholders, which describes the expected position of each WMR formation. By using placeholders, you can get the desired track for each WMR. In addition, in order to avoid the collision between multiple WMR in the group, we add an attraction to the trajectory tracking method. Because MWMR in the same team have different attractions, collisions can be easily avoided. Through simulation analysis, it is proved that the optimization model is reasonable and correct. In the last part, the limitations of this model and corresponding suggestions are given

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

Path planning, modelling and simulation for energy optimised mobile robotics

This thesis is concerned with an investigation of a solution for mobile robotic platforms to minimize the usage of scarce energy that is available and is not wasted following traditionally planned paths for complex terrain environments. This therefore addresses the need to reduce the total energy cost during a field task or mission. A path planning algorithm is designed by creating a new approach of artificial potential field method that generates a planned path, utilising terrain map. The new approach has the capability of avoiding the local minimum problems which is one of the major problems of traditional potential field method. By solving such problems gives a reliable solution to establish a required path. Therefore the approach results in an energy efficient path of the terrain identified, instead obvious straight line of the terrain. A literature review is conducted which reviews the mainstream path planning algorithms with the applications in mobile robotic platforms was analysed. These path planning algorithms are compared for the purpose of energy optimized planning, which concludes the method of artificial potential field as the path planning algorithm which has the most potential and will be further investigated and improved in this research. The methodology of designing, modelling and simulating a mobile robotic platform is defined and presented for the purpose of energy optimized path planning requirement. The research is to clarify the needs, requirements, and specifications of the design. A complete set of models which include mechanical and electrical modelling, functional concept modelling, modelling of the system are established. Based on these models, an energy optimized path planning algorithm is designed. The modelling of force and the kinematics is established to validate and evaluate the result of the algorithm through simulations. Moreover a simulation environment is established which is constructed for multi perspective simulation. This also enables collaborative simulation using Simulink and ADAMS to for simulating a path generated by the path planning algorithm and assess the energy consumption of the driven and steering mechanism of an exemplar system called AgriRover. This simulation environment allows the capture of simulated result of the total energy consumption, therefore outlines the energy cost behaviour of the AgriRover. A total of two sets of paths was tested in the fields for validation, one being generated by the energy optimized path planning algorithm and the other following a straight path. During the field tests the total cost of energy was captured . Two sets of results are compared with each other and compared with the simulation. The comparison shows a 21.34% of the energy saving by deploying the path generated with the energy optimized path planning algorithm in the field test. This research made the following contribution to knowledge. A comparison and grading of mainstream path planning algorithms from energy optimisation perspective is undertaken using detailed evaluation criteria, including computational power required, extendibility, flexibility and more criteria that is relevant for the energy optimized planning purpose. These algorithms have not been compared from energy optimisation angle before, and the research for energy optimised planning under complex terrain environments have not been investigated. Addressing these knowledge gaps, a methodology of designing, modelling and simulating a mobile platform system is proposed to facilitate an energy optimized path planning. This , leads to a new approach of path planning algorithm that reduces unnecessary energy spend for climbing of the terrain, using the terrain data available. Such a methodology derives several novel methods: Namely, a method for avoiding local minimum problem for artificial potential field path planning using the approach of approximation; A method of achieving high expendability of the path planning algorithm, where this method is capable of generate a path through a large map in a short time; A novel method of multi perspective dynamic simulation, which is capable of simulating the behaviour of internal mechanism and the overall robotic mobile platform with the fully integrated control, The dynamic simulation enables prediction of energy consumption; Finally, a novel method of mathematically modelling and simplifying a steering mechanism for the wheel based mobile vehicle was further investigated.This thesis is concerned with an investigation of a solution for mobile robotic platforms to minimize the usage of scarce energy that is available and is not wasted following traditionally planned paths for complex terrain environments. This therefore addresses the need to reduce the total energy cost during a field task or mission. A path planning algorithm is designed by creating a new approach of artificial potential field method that generates a planned path, utilising terrain map. The new approach has the capability of avoiding the local minimum problems which is one of the major problems of traditional potential field method. By solving such problems gives a reliable solution to establish a required path. Therefore the approach results in an energy efficient path of the terrain identified, instead obvious straight line of the terrain. A literature review is conducted which reviews the mainstream path planning algorithms with the applications in mobile robotic platforms was analysed. These path planning algorithms are compared for the purpose of energy optimized planning, which concludes the method of artificial potential field as the path planning algorithm which has the most potential and will be further investigated and improved in this research. The methodology of designing, modelling and simulating a mobile robotic platform is defined and presented for the purpose of energy optimized path planning requirement. The research is to clarify the needs, requirements, and specifications of the design. A complete set of models which include mechanical and electrical modelling, functional concept modelling, modelling of the system are established. Based on these models, an energy optimized path planning algorithm is designed. The modelling of force and the kinematics is established to validate and evaluate the result of the algorithm through simulations. Moreover a simulation environment is established which is constructed for multi perspective simulation. This also enables collaborative simulation using Simulink and ADAMS to for simulating a path generated by the path planning algorithm and assess the energy consumption of the driven and steering mechanism of an exemplar system called AgriRover. This simulation environment allows the capture of simulated result of the total energy consumption, therefore outlines the energy cost behaviour of the AgriRover. A total of two sets of paths was tested in the fields for validation, one being generated by the energy optimized path planning algorithm and the other following a straight path. During the field tests the total cost of energy was captured . Two sets of results are compared with each other and compared with the simulation. The comparison shows a 21.34% of the energy saving by deploying the path generated with the energy optimized path planning algorithm in the field test. This research made the following contribution to knowledge. A comparison and grading of mainstream path planning algorithms from energy optimisation perspective is undertaken using detailed evaluation criteria, including computational power required, extendibility, flexibility and more criteria that is relevant for the energy optimized planning purpose. These algorithms have not been compared from energy optimisation angle before, and the research for energy optimised planning under complex terrain environments have not been investigated. Addressing these knowledge gaps, a methodology of designing, modelling and simulating a mobile platform system is proposed to facilitate an energy optimized path planning. This , leads to a new approach of path planning algorithm that reduces unnecessary energy spend for climbing of the terrain, using the terrain data available. Such a methodology derives several novel methods: Namely, a method for avoiding local minimum problem for artificial potential field path planning using the approach of approximation; A method of achieving high expendability of the path planning algorithm, where this method is capable of generate a path through a large map in a short time; A novel method of multi perspective dynamic simulation, which is capable of simulating the behaviour of internal mechanism and the overall robotic mobile platform with the fully integrated control, The dynamic simulation enables prediction of energy consumption; Finally, a novel method of mathematically modelling and simplifying a steering mechanism for the wheel based mobile vehicle was further investigated

Arquitectura de software para navegación autónoma y coordinada de enjambres de drones en labores de lucha contra incendios forestales y urbanos

Los progresos alcanzados dentro del área de los Vehículos Aéreos No Tripulados, conocidos comúnmente como drones, han ocasionado un aumento exponencial de su mercado, gracias, principalmente, al desarrollo e implementación de soluciones tecnológicas innovadoras. La capacidad de este tipo de aeronaves de poder embarcar un gran abanico de sensores provoca que, en la actualidad, se oriente el uso de esta tecnología a un amplio conjunto de aplicaciones y servicios, como son las emergencias y, en concreto, aquellas relacionadas con los incendios, tanto forestales como urbanos. La aparición y crecimiento de empresas, como Drone Hopper S.L, cuya labor se destina al diseño y fabricación de drones de alta capacidad de carga y autonomía destinados a la lucha contra el fuego han provocado que dichas plataformas aéreas se posicionen como una potente y eficaz herramienta en el campo de las emergencias y la seguridad. Actualmente, la empresa Drone Hopper se encuentra inmersa en el diseño y desarrollo de la plataforma WILD HOPPER, capaz de trasladar hasta 600 litros de carga útil y realizar maniobras eficaces en labores de extinción de incendios, gracias, en gran parte, a su sistema patentado de liberación de líquidos. Junto a este sistema, los drones fabricados por Drone Hopper, presentan la ventaja de poder realizar trabajos durante la noche, complementando los trabajos de los medios aéreos tradicionales y, en conjunto, superando las limitaciones de otras plataformas aéreas no tripuladas, cuyo uso en trabajos relacionados con los incendios se limitan a la monitorización y vigilancia de áreas de interés. En los últimos años, se ha producido el nacimiento y expansión de los denominados enjambres de drones, o lo que es lo mismo, equipos escalables de varias aeronaves no tripuladas que operan de manera coordinada y que permiten explotar el uso de tecnologías como la desarrollada por la empresa Drone Hopper. Actualmente, la expansión de estos sistemas es fruto del crecimiento en las investigaciones y desarrollos dentro de este campo, ocasionado, principalmente, por las ventajas que presentan los enjambres de drones en términos de robustez, versatilidad y eficacia. La posibilidad de poder desplegar en un mismo área un conjunto de drones que realicen tareas de manera coordinada provoca, en primer lugar, que se disponga de una herramienta robusta contra averías, en la que la pérdida de cualquiera de los drones intervinientes en la misión no implicaría el fracaso de la misma. Y, en segundo lugar, que se establezca una actuación eficaz ligada a la reducción del tiempo de respuesta y, a la posibilidad de acometer diferentes tareas de manera simultánea. Junto a esto, destacar que el enjambre de drones no está únicamente relacionado al uso de aeronaves no tripuladas con similares características, sino que existe la posibilidad de emplear equipos heterogéneos de drones, o lo que es lo mismo, desplegar sobre un mismo escenario drones con diferentes características, tanto a nivel estructural como de carga de pago, lo que origina que se disponga de una herramienta tecnológica de alta versatilidad. Estas tres características convierten a los enjambres de drones en una herramienta tecnológica de alto valor añadido en trabajos relacionados con la lucha contra el fuego, los cuales se caracterizan por el dinamismo, la adversidad, condiciones extremas y rápidamente cambiantes, en las que el uso de sistemas robustos y versátiles presentan una alta aplicabilidad. Aunque junto a estas propiedades existe un aspecto, el cual sigue constituyendo un campo de estudio, investigación y desarrollo, como es la navegación autónoma y cooperativa de dichos enjambres, lo que permitiría poder emplear esta tecnología sin supervisión humana en la zona, reduciendo de esta manera el riesgo y exposición de vidas humanas. Por este motivo, a lo largo del presente trabajo se desarrolla e implementa una arquitectura de software multi-capa capaz de permitir la navegación autónoma y coordinada de un enjambre de drones para poder acometer trabajos esenciales en la lucha contra el fuego, tanto en áreas urbanas como forestales. La arquitectura propuesta incluye un conjunto de métodos redundantes y complementarios que permiten establecer diferentes capas de control para permitir la navegación sin supervisión y cooperativa del enjambre. La primera de las capas consiste en un planificador de trayectorias, basado en información del entorno en 2D y en 3D, que permite dotar a la arquitectura de un método eficiente y escalable que genere como solución un conjunto de trayectorias óptimas y seguras para que cada uno de los drones pueda alcanzar una ubicación determinada en el entorno. Junto a la efectividad y la escalabilidad, el método propuesto se caracteriza por ser altamente configurable, la cual permite la generación de trayectorias en diferentes situaciones, entre las que destaca la posibilidad de establecer una solución que permita al enjambre de drones alcanzar un objetivo en cuestión bajo una formación concreta, de cara a realizar labores de extinción de manera más eficaz. La segunda de las capas consta de un gestor de colisiones, formado por diferentes desarrollos y algoritmos, que dotan al enjambre de un sistema de detección y evasión de obstáculos, tanto entre drones del enjambre como con obstáculos presentes en el entorno, que garanticen la navegación segura y libre de colisiones de cada uno de los agentes del enjambre. Por último, la arquitectura de software desarrollada en la presente tesis doctoral busca dotar a cada agente del enjambre de un modelo de toma de decisiones inteligente, el cual permita a cada aeronave, de manera autónoma, escoger una secuencia de acciones que le permita alcanzar un objetivo concreto. Este modelo inteligente de toma de decisiones complementa a todos los métodos de la arquitectura propuesta y, permite, de manera redundante establecer un desarrollo adicional que garantice la navegación autónoma del enjambre en entornos dinámicos. La combinación de estos desarrollos bajo una misma arquitectura provoca el despliegue de una flota de drones capaz de navegar y realizar trabajos de manera autónoma y cooperativa sobre entornos adversos y dinámicos, como es el caso de los incendios. Por tanto, los trabajos y desarrollos de la presente tesis doctoral se centran en crear una herramienta tecnológica de alto valor añadido, a partir del desarrollo de arquitecturas de software embarcadas en un enjambre escalable de drones que, trabajando de manera coordinada, establezca una respuesta rápida, eficiente y robusta al problema de los incendios, tanto forestales como urbanos.The progress achieved in the area of Unmanned Aerial Vehicles, commonly known as drones, has caused an exponential increase in its market, mainly thanks to the development and implementation of innovative technological solutions. The capacity of this type of aircraft to be able to embark on a wide range of sensors means that, at present, the use of this technology is directed at a wide range of applications and services, such as emergencies and, specifically, those related to fires, both forest and urban. The appearance and growth of companies such as Drone Hopper S.L., whose work is aimed at the design and manufacture of high load capacity and autonomy drones for firefighting, has led to these aerial platforms being positioned as a powerful and effective tool in the field of emergencies and security. Currently, Dron Hopper is immersed in the design and development of the WILD HOPPER platform, capable of carrying up to 600 liters of payload and perform effective maneuvers in fire fighting, thanks largely to its patented system of liquid release. Together with the system, the drones manufactured by Drone Hopper have the advantage of being able to carry out work at night, complementing the work of traditional aerial means and, as a whole, overcoming the limitations of other unmanned aerial platforms, whose use in fire-related work is limited to the monitoring and surveillance of areas of interest. In recent years, there has been the birth and expansion of the so-called drone swarms, or what is the same, scalable equipment from various unmanned aircraft that operate in a coordinated manner and that allow the use of technologies such as the one developed by the Drone Hopper company. Currently, the expansion of these systems is the result of the growth in research and development within this field, caused mainly by the advantages that drone swarms present in terms of robustness, versatility, and efficiency. The possibility of being able to deploy in the same area a set of drones that carry out tasks in a coordinated way causes, in the first place, that a robust tool against breakdowns is available, in which the loss of any of the drones involved in the mission would not imply the failure of the same one. And, secondly, that an effective action is established, linked to the reduction of the response time and to the possibility of undertaking different tasks simultaneously. In addition to this, it is important to point out that the swarm of drones is not only related to the use of unmanned aircraft with similar characteristics, but that there is also the possibility of using heterogeneous drone teams, or what is the same, deploying drones with different characteristics on the same stage, both at a structural and payload level, which results in a highly versatile technological tool. These three characteristics make drone swarms a high value-added technological tool in firefighting related work, which is characterized by dynamism, adversity, extreme and rapidly changing conditions, in which the use of robust and versatile systems have high applicability. Although together with these properties there is an aspect, which continues to be a field of study, research, and development, as is the autonomous and cooperative navigation of these swarms, which would allow the use of this technology without human supervision in the area, thus reducing the risk and exposure of human lives. For this reason, throughout the present work, multi-layer software architecture is developed and implemented that is capable of allowing the autonomous and coordinated navigation of a swarm of drones to be able to undertake essential fire-fighting work, both in urban and forest areas. The proposed architecture includes a set of redundant and complementary methods that allow establishing different control layers to enable unsupervised and cooperative navigation of the swarm. The first layer consists of a path planner, based on 2D and 3D environmental information, which provides the architecture with an efficient and scalable method that generates a set of optimal and safe paths as a solution so that each of the drones can reach a particular location in the environment. Along with the effectiveness and scalability, the proposed method is characterized by being highly configurable, which allows the generation of trajectories in different situations, among which highlights the possibility of establishing a solution that allows the swarm of drones to reach a target in question under a specific training, to perform extinction work more effectively. The second of the layers consists of a collision manager, formed by different developments and algorithms, which provide the swarm with a system for detecting and avoiding obstacles, both between drones of the swarm and with obstacles present in the environment, to ensure safe and collision-free navigation of each of the agents of the swarm. Finally, the software architecture developed in this doctoral thesis seeks to provide each swarm agent with an intelligent decision-making model, which allows each aircraft, autonomously, to choose a sequence of actions that will allow it to reach a speciffic objective. This intelligent decision-making model complements all the methods of the proposed architecture and, redundantly, allows the establishment of additional development that guarantees the autonomous navigation of the swarm in dynamic environments. The combination of these developments under the same architecture results in the deployment of a fleet of drones capable of navigating and working autonomously and cooperatively in adverse and dynamic environments, such as fires. Therefore, the work and developments of this doctoral thesis are focused on creating a technological tool with high added value, from the development of software architectures embedded in a scalable swarm of drones that, working in a coordinated manner, establish a rapid, efficient and robust response to the problem of fires, both forest and urban.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Pascual Campoy Cervera.- Secretario: Javier Fernández Andrés.- Vocal: Walterio W. Mayol-Cueva