6 research outputs found
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 navegaci贸n aut贸noma y coordinada de enjambres de drones en labores de lucha contra incendios forestales y urbanos
Los progresos alcanzados dentro del 谩rea de los Veh铆culos A茅reos No Tripulados,
conocidos com煤nmente como drones, han ocasionado un aumento exponencial
de su mercado, gracias, principalmente, al desarrollo e implementaci贸n de soluciones
tecnol贸gicas 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 tecnolog铆a 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 aparici贸n y crecimiento de empresas, como Drone Hopper S.L, cuya labor se
destina al dise帽o y fabricaci贸n de drones de alta capacidad de carga y autonom铆a
destinados a la lucha contra el fuego han provocado que dichas plataformas a茅reas
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 dise帽o y desarrollo de la plataforma WILD HOPPER, capaz de trasladar
hasta 600 litros de carga 煤til y realizar maniobras eficaces en labores de extinci贸n
de incendios, gracias, en gran parte, a su sistema patentado de liberaci贸n de l铆quidos.
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 a茅reos tradicionales y, en conjunto, superando las limitaciones de
otras plataformas a茅reas no tripuladas, cuyo uso en trabajos relacionados con los
incendios se limitan a la monitorizaci贸n y vigilancia de 谩reas de inter茅s.
En los 煤ltimos a帽os, se ha producido el nacimiento y expansi贸n 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 tecnolog铆as como la desarrollada por la empresa Drone Hopper. Actualmente,
la expansi贸n 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 t茅rminos de robustez, versatilidad y eficacia. La posibilidad de poder desplegar en un mismo 谩rea un conjunto de drones
que realicen tareas de manera coordinada provoca, en primer lugar, que se disponga
de una herramienta robusta contra aver铆as, en la que la p茅rdida de cualquiera de los drones intervinientes en la misi贸n no implicar铆a el fracaso de la misma. Y, en segundo lugar, que se establezca una actuaci贸n eficaz ligada a la reducci贸n del
tiempo de respuesta y, a la posibilidad de acometer diferentes tareas de manera
simult谩nea. Junto a esto, destacar que el enjambre de drones no est谩 煤nicamente
relacionado al uso de aeronaves no tripuladas con similares caracter铆sticas, sino
que existe la posibilidad de emplear equipos heterog茅neos de drones, o lo que es lo
mismo, desplegar sobre un mismo escenario drones con diferentes caracter铆sticas,
tanto a nivel estructural como de carga de pago, lo que origina que se disponga de
una herramienta tecnol贸gica de alta versatilidad. Estas tres caracter铆sticas convierten
a los enjambres de drones en una herramienta tecnol贸gica de alto valor a帽adido
en trabajos relacionados con la lucha contra el fuego, los cuales se caracterizan por
el dinamismo, la adversidad, condiciones extremas y r谩pidamente cambiantes, en
las que el uso de sistemas robustos y vers谩tiles presentan una alta aplicabilidad.
Aunque junto a estas propiedades existe un aspecto, el cual sigue constituyendo
un campo de estudio, investigaci贸n y desarrollo, como es la navegaci贸n aut贸noma
y cooperativa de dichos enjambres, lo que permitir铆a poder emplear esta tecnolog铆a sin supervisi贸n humana en la zona, reduciendo de esta manera el riesgo y
exposici贸n 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 navegaci贸n aut贸noma y coordinada de un enjambre de drones para poder
acometer trabajos esenciales en la lucha contra el fuego, tanto en 谩reas urbanas
como forestales. La arquitectura propuesta incluye un conjunto de m茅todos redundantes
y complementarios que permiten establecer diferentes capas de control para
permitir la navegaci贸n sin supervisi贸n y cooperativa del enjambre.
La primera de las capas consiste en un planificador de trayectorias, basado en
informaci贸n del entorno en 2D y en 3D, que permite dotar a la arquitectura de un
m茅todo eficiente y escalable que genere como soluci贸n un conjunto de trayectorias
贸ptimas y seguras para que cada uno de los drones pueda alcanzar una ubicaci贸n
determinada en el entorno. Junto a la efectividad y la escalabilidad, el m茅todo
propuesto se caracteriza por ser altamente configurable, la cual permite la generaci贸n de trayectorias en diferentes situaciones, entre las que destaca la posibilidad
de establecer una soluci贸n que permita al enjambre de drones alcanzar un objetivo
en cuesti贸n bajo una formaci贸n concreta, de cara a realizar labores de extinci贸n
de manera m谩s 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 detecci贸n
y evasi贸n de obst谩culos, tanto entre drones del enjambre como con obst谩culos presentes
en el entorno, que garanticen la navegaci贸n segura y libre de colisiones de
cada uno de los agentes del enjambre.
Por 煤ltimo, 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 aut贸noma, escoger una
secuencia de acciones que le permita alcanzar un objetivo concreto. Este modelo
inteligente de toma de decisiones complementa a todos los m茅todos de la arquitectura
propuesta y, permite, de manera redundante establecer un desarrollo adicional
que garantice la navegaci贸n aut贸noma del enjambre en entornos din谩micos.
La combinaci贸n de estos desarrollos bajo una misma arquitectura provoca el
despliegue de una flota de drones capaz de navegar y realizar trabajos de manera
aut贸noma y cooperativa sobre entornos adversos y din谩micos, como es el caso de
los incendios. Por tanto, los trabajos y desarrollos de la presente tesis doctoral se
centran en crear una herramienta tecnol贸gica de alto valor a帽adido, a partir del
desarrollo de arquitecturas de software embarcadas en un enjambre escalable de
drones que, trabajando de manera coordinada, establezca una respuesta r谩pida,
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 Ingenier铆a El茅ctrica, Electr贸nica y Autom谩tica por la Universidad Carlos III de MadridPresidente: Pascual Campoy Cervera.- Secretario: Javier Fern谩ndez Andr茅s.- Vocal: Walterio W. Mayol-Cueva