121 research outputs found
Automated Planning for Urban Traffic Control: Strategic Vehicle Routing to Respect Air Quality Limitations
The global growth in urbanisation increases the demand for services including road transport infrastructure, presenting challenges in terms of mobility. These trends are occurring in the context of concerns around environmental issues of poor air quality and transport related carbon dioxide emissions. One out of several ways to help meet these challenges is in the intelligent routing of road traffic through congested urban areas. Our goal is to show the feasibility of using automated planning to perform this routing, taking into account a knowledge of vehicle types, vehicle emissions, route maps, air quality zones, etc. Specifically focusing on air quality concerns, in this paper we investigate the problem where the goals are to minimise overall vehicle delay while utilising network capacity fully, and respecting air quality limits. We introduce an automated planning approach for the routing of traffic to address these areas. The approach has been evaluated on micro-simulation models that use real-world data supplied by our industrial partner. Results show the feasibility of using AI planning technology to deliver efficient routes for vehicles that avoid the breaking of air quality limits, and that balance traffic flow through the network
Route planning algorithms: Planific@ Project
Planific@ is a route planning project for the city of
Madrid (Spain). Its main aim is to develop an intelligence system
capable of routing people from one place in the city to any other
using the public transport. In order to do this, it is necessary to
take into account such things as: time, traffic, user preferences,
etc. Before beginning to design the project is necessary to make
a comprehensive study of the variety of main known route
planning algorithms suitable to be used in this projec
Architecture for planning and execution of missions with fleets of unmanned vehicles
Esta tesis presenta contribuciones en el campo de la planificación automática y la
programación de tareas, la rama de la inteligencia artificial que se ocupa de la
realización de estrategias o secuencias de acciones típicamente para su ejecución por
parte de vehículos no tripulados, robots autónomos y/o agentes inteligentes. Cuando se
intenta alcanzar un objetivo determinado, la cooperación puede ser un aspecto clave. La
complejidad de algunas tareas requiere la cooperación entre varios agentes. Mas aún,
incluso si una tarea es lo suficientemente simple para ser llevada a cabo por un único
agente, puede usarse la cooperación para reducir el coste total de la misma. Para realizar
tareas complejas que requieren interacción física con el mundo real, los vehículos no
tripulados pueden ser usados como agentes. En los últimos años se han creado y utilizado
una gran diversidad de plataformas no tripuladas, principalmente vehículos que pueden
ser dirigidos sin un humano a bordo, tanto en misiones civiles como militares.
En esta tesis se aborda la aplicación de planificación simbólica de redes jerárquicas
de tareas (HTN planning, por sus siglas en inglés) en la resolución de problemas de
enrutamiento de vehículos (VRP, por sus siglas en inglés) [18], en dominios que implican
múltiples vehículos no tripulados de capacidades heterogéneas que deben cooperar para
alcanzar una serie de objetivos específicos.
La planificación con redes jerárquicas de tareas describe dominios utilizando una
descripción que descompone conjuntos de tareas en subconjuntos más pequeños de
subtareas gradualmente, hasta obtener tareas del más bajo nivel que no pueden ser
descompuestas y se consideran directamente ejecutables. Esta jerarquía es similar al modo
en que los humanos razonan sobre los problemas, descomponiéndolos en subproblemas
según el contexto, y por lo tanto suelen ser fáciles de comprender y diseñar.
Los problemas de enrutamiento de vehículos son una generalización del problema del
viajante (TSP, por sus siglas en inglés). La resolución del problema del viajante consiste
en encontrar la ruta más corta posible que permite visitar una lista de ciudades, partiendo
y acabando en la misma ciudad. Su generalización, el problema de enrutamiento de
vehículos, consiste en encontrar el conjunto de rutas de longitud mínima que permite
cubrir todas las ciudades con un determinado número de vehículos. Ambos problemas
cuentan con una fuerte componente combinatoria para su resolución, especialmente en el caso del VRP, por lo que su presencia en dominios que van a ser tratados con un planificador
HTN clásico supone un gran reto.
Para la aplicación de un planificador HTN en la resolución de problemas de enrutamiento
de vehículos desarrollamos dos métodos. En el primero de ellos presentamos un sistema
de optimización de soluciones basado en puntuaciones, que nos permite una nueva forma
de conexión entre un software especializado en la resolución del VRP con el planificador
HTN. Llamamos a este modo de conexión el método desacoplado, puesto que resolvemos
la componente combinatoria del problema de enrutamiento de vehículos mediante un
solucionador específico que se comunica con el planificador HTN y le suministra la
información necesaria para continuar con la descomposición de tareas. El segundo método
consiste en mejorar el planificador HTN utilizado para que sea capaz de resolver el
problema de enrutamiento de vehículos de la mejor forma posible sin tener que depender
de módulos de software externos. Llamamos a este modo el método acoplado. Con
este motivo hemos desarrollado un nuevo planificador HTN que utiliza un algoritmo de
búsqueda distinto del que se utiliza normalmente en planificadores de este tipo.
Esta tesis presenta nuevas contribuciones en el campo de la planificación con redes
jerárquicas de tareas para la resolución de problemas de enrutamiento de vehículos. Se
aplica una nueva forma de conexión entre dos planificadores independientes basada en
un sistema de cálculo de puntuaciones que les permite colaborar en la optimización de
soluciones, y se presenta un nuevo planificador HTN con un algoritmo de búsqueda distinto
al comúnmente utilizado. Se muestra la aplicación de estos dos métodos en misiones
civiles dentro del entorno de los Proyectos ARCAS y AEROARMS financiados por la
Comisión Europea y se presentan extensos resultados de simulación para comprobar la
validez de los dos métodos propuestos.This thesis presents contributions in the field of automated planning and scheduling,
the branch of artificial intelligence that concerns the realization of strategies or
action sequences typically for execution by unmanned vehicles, autonomous robots and/or
intelligent agents. When trying to achieve certain goal, cooperation may be a key aspect.
The complexity of some tasks requires the cooperation among several agents. Moreover,
even if the task is simple enough to be carried out by a single agent, cooperation can be
used to decrease the overall cost of the operation. To perform complex tasks that require
physical interaction with the real world, unmanned vehicles can be used as agents. In the
last years a great variety of unmanned platforms, mainly vehicles that can be driven without
a human on board, have been developed and used both in civil and military missions.
This thesis deals with the application of Hierarchical Task Network (HTN) planning
in the resolution of vehicle routing problems (VRP) [18] in domains involving multiple
heterogeneous unmanned vehicles that must cooperate to achieve specific goals.
HTN planning describes problem domains using a description that decomposes set of
tasks into subsets of smaller tasks and so on, obtaining low-level tasks that cannot be
further decomposed and are supposed to be executable. The hierarchy resembles the way
the humans reason about problems by decomposing them into sub-problems depending
on the context and therefore tend to be easy to understand and design.
Vehicle routing problems are a generalization of the travelling salesman problem (TSP).
The TSP consists on finding the shortest path that connects all the cities from a list, starting
and ending on the same city. The VRP consists on finding the set of minimal routes that
cover all cities by using a specific number of vehicles. Both problems have a combinatorial
nature, specially the VRP, that makes it very difficult to use a HTN planner in domains
where these problems are present.
Two approaches to use a HTN planner in domains involving the VRP have been tested.
The first approach consists on a score-based optimization system that allows us to apply a
new way of connecting a software specialized in the resolution of the VRP with the HTN
planner. We call this the decoupled approach, as we tackle the combinatorial nature of the
VRP by using a specialized solver that communicates with the HTN planner and provides
all the required information to do the task decomposition. The second approach consists on improving and enhancing the HTN planner to be capable of solving the VRP without
needing the use of an external software. We call this the coupled approach. For this reason,
a new HTN planner that uses a different search algorithm from these commonly used in
that type of planners has been developed and is presented in this work.
This thesis presents new contributions in the field of hierarchical task network planning
for the resolution of vehicle routing problem domains. A new way of connecting two
independent planning systems based on a score calculation system that lets them cooperate
in the optimization of the solutions is applied, and a new HTN planner that uses a different
search algorithm from that usually used in other HTN planners is presented. These two
methods are applied in civil missions in the framework of the ARCAS and AEROARMS
Projects funded by the European Commission. Extensive simulation results are presented
to test the validity of the two approaches
Algorithms for multi-robot systems on the cooperative exploration & last-mile delivery problems
La aparición de los vehículos aéreos no tripulados (UAVs) y de los vehículos terrestres no tripulados (UGVs) ha llevado a la comunidad científica a enfrentarse a problemas ideando paradigmas de cooperación con UGVs y UAVs. Sin embargo, no suele ser trivial determinar si la cooperación entre UGVs y UAVs es adecuada para un determinado problema. Por esta razón, en esta tesis, investigamos un paradigma particular de cooperación UGV-UAV en dos problemas de la literatura, y proponemos un controlador autónomo para probarlo en escenarios simulados.
Primero, formulamos un problema particular de exploración cooperativa que consiste en alcanzar un conjunto de puntos de destino en un área de exploración a gran escala. Este problema define al UGV como una estación de carga móvil para transportar el UAV a través de diferentes lugares desde donde el UAV puede alcanzar los puntos de destino. Por consiguiente, proponemos el algoritmo TERRA para resolverlo. Este algoritmo se destaca por dividir el problema de exploración en cinco subproblemas, en los que cada subproblema se resuelve en una etapa particular del algoritmo.
Debido a la explosión de la entrega de paquetes en las empresas de comercio electrónico, formulamos también una generalización del conocido problema de la entrega en la última milla. En este caso, el UGV actúa como una estación de carga móvil que transporta a los paquetes y a los UAVs, y estos se encargan de entregarlos. De esta manera, seguimos la estrategia de división descrita por TERRA, y proponemos el algoritmo COURIER. Este algoritmo replica las cuatro primeras etapas de TERRA, pero construye una nueva quinta etapa para producir un plan de tareas que resuelva el problema. Para evaluar el paradigma de cooperación UGV-UAV en escenarios simulados, proponemos el controlador autónomo ARIES. Este controlador sigue un enfoque jerárquico descentralizado de líder-seguidor para integrar cualquier paradigma de cooperación de manera distribuida.
Ambos algoritmos han sido caracterizados para identificar los aspectos relevantes del paradigma de cooperación en los problemas relacionados. Además, ambos demuestran un gran rendimiento del paradigma de cooperación en tales problemas, y al igual que el controlador autónomo, revelan un gran potencial para futuras aplicaciones reales.The emergence of Unmanned Aerial Vehicles (UAVs) and Unmanned
Ground Vehicles (UGVs) has conducted the research community to
face historical complex problems by devising UGV-UAV cooperation
paradigms. However, it is usually not a trivial task to determine
whether or not a UGV-UAV cooperation is suitable for a particular
problem. For this reason, in this thesis, we investigate a particular
UGV-UAV cooperation paradigm over two problems in the literature,
and we propose an autonomous controller to test it on simulated
scenarios.
Driven by the planetary exploration, we formulate a particular cooperative
exploration problem consisting of reaching a set of target
points in a large-scale exploration area. This problem defines the UGV
as a moving charging station to carry the UAV through different locations
from where the UAV can reach the target points. Consequently,
we propose the cooperaTive ExploRation Routing Algorithm (TERRA)
to solve it. This algorithm stands out for splitting up the exploration
problem into five sub-problems, in which each sub-problem is solved
in a particular stage of the algorithm. In the same way, driven by the
explosion of parcels delivery in e-commerce companies, we formulate
a generalization of the well-known last-mile delivery problem. This
generalization defines the same UGV’s and UAV’s rol as the exploration
problem. That is, the UGV acts as a moving charging station
which carries the parcels along several UAVs to deliver them. In this
way, we follow the split strategy depicted by TERRA to propose the
COoperative Unmanned deliveRIEs planning algoRithm (COURIER).
This algorithm replicates the first four TERRA’s stages, but it builds a
new fifth stage to produce a task plan solving the problem. In order to
evaluate the UGV-UAV cooperation paradigm on simulated scenarios,
we propose the Autonomous coopeRatIve Execution System (ARIES).
This controller follows a hierarchical decentralized leader-follower approach
to integrate any cooperation paradigm in a distributed manner.
Both algorithms have been characterized to identify the relevant
aspects of the cooperation paradigm in the related problems. Also,
both of them demonstrate a great performance of the cooperation
paradigm in such problems, and as well as the autonomous controller,
reveal a great potential for future real applications
Novelty Accommodating Multi-Agent Planning in High Fidelity Simulated Open World
Autonomous agents acting in real-world environments often need to reason with
unknown novelties interfering with their plan execution. Novelty is an
unexpected phenomenon that can alter the core characteristics, composition, and
dynamics of the environment. Novelty can occur at any time in any sufficiently
complex environment without any prior notice or explanation. Previous studies
show that novelty has catastrophic impact on agent performance. Intelligent
agents reason with an internal model of the world to understand the intricacies
of their environment and to successfully execute their plans. The introduction
of novelty into the environment usually renders their internal model inaccurate
and the generated plans no longer applicable. Novelty is particularly prevalent
in the real world where domain-specific and even predicted novelty-specific
approaches are used to mitigate the novelty's impact. In this work, we
demonstrate that a domain-independent AI agent designed to detect,
characterize, and accommodate novelty in smaller-scope physics-based games such
as Angry Birds and Cartpole can be adapted to successfully perform and reason
with novelty in realistic high-fidelity simulator of the military domain
Efficient Macroscopic Urban Traffic Models for Reducing Congestion: a PDDL+ Planning Approach
The global growth in urbanisation increases the demand for services including road transport infrastructure, presenting challenges in terms of mobility. In this scenario, optimising the exploitation of urban road networks is a pivotal challenge. Existing urban traffic control approaches, based on complex mathematical models, can effectively deal with planned-ahead events, but are not able to cope with unexpected situations –such as roads blocked due to car accidents or weather-related events– because of their huge computational requirements. Therefore, such unexpected situations are mainly dealt with manually, or by exploiting pre-computed policies.
Our goal is to show the feasibility of using mixed discrete-continuous planning to deal with unexpected circumstances in urban traffic control. We present a PDDL+ formulation of urban traffic control, where continuous processes are used to model flows of cars, and show how planning can be used to efficiently reduce congestion of specified roads by controlling traffic light green phases. We present simulation results on two networks (one of them considers Manchester city centre) that demonstrate the effectiveness of the approach, compared with fixed-time and reactive techniques
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