11,415 research outputs found
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Automated sequence and motion planning for robotic spatial extrusion of 3D trusses
While robotic spatial extrusion has demonstrated a new and efficient means to
fabricate 3D truss structures in architectural scale, a major challenge remains
in automatically planning extrusion sequence and robotic motion for trusses
with unconstrained topologies. This paper presents the first attempt in the
field to rigorously formulate the extrusion sequence and motion planning (SAMP)
problem, using a CSP encoding. Furthermore, this research proposes a new
hierarchical planning framework to solve the extrusion SAMP problems that
usually have a long planning horizon and 3D configuration complexity. By
decoupling sequence and motion planning, the planning framework is able to
efficiently solve the extrusion sequence, end-effector poses, joint
configurations, and transition trajectories for spatial trusses with
nonstandard topologies. This paper also presents the first detailed computation
data to reveal the runtime bottleneck on solving SAMP problems, which provides
insight and comparing baseline for future algorithmic development. Together
with the algorithmic results, this paper also presents an open-source and
modularized software implementation called Choreo that is machine-agnostic. To
demonstrate the power of this algorithmic framework, three case studies,
including real fabrication and simulation results, are presented.Comment: 24 pages, 16 figure
Multi-resolution mapping and planning for UAV navigation in attitude constrained environments
In this thesis we aim to bridge the gap between high quality map reconstruction and Unmanned Aerial Vehicles (UAVs) SE(3) motion planning in challenging environments with narrow openings, such as disaster areas, which requires attitude to be considered. We propose an efficient system that leverages the concept of adaptive-resolution volumetric mapping, which naturally integrates with the hierarchical decomposition of space in an octree data structure. Instead of a Truncated Signed Distance Function (TSDF), we adopt mapping of occupancy probabilities in log-odds representation, which allows representation of both surfaces, as well as the entire free, i.e.\ observed space, as opposed to unobserved space. We introduce a method for choosing resolution -on the fly- in real-time by means of a multi-scale max-min pooling of the input depth image. The notion of explicit free space mapping paired with the spatial hierarchy in the data structure, as well as map resolution, allows for collision queries, as needed for robot motion planning, at unprecedented speed. Our mapping strategy supports pinhole cameras as well as spherical sensor models. Additionally, we introduce a first-of-a-kind global minimum cost path search method based on A* that considers attitude along the path. State-of-the-art methods incorporate attitude only in the refinement stage. To make the problem tractable, our method exploits an adaptive and coarse-to-fine approach using global and local A* runs, plus an efficient method to introduce the UAV attitude in the process. We integrate our method with an SE(3) trajectory optimisation method based on a safe-flight-corridor, yielding a complete path planning pipeline.
We quantitatively evaluate our mapping strategy in terms of mapping accuracy, memory, runtime performance, and planning performance showing improvements over the state-of-the-art, particularly in cases requiring high resolution maps. Furthermore, extensive evaluation is undertaken using the AirSim flight simulator under closed loop control in a set of randomised maps, allowing us to quantitatively assess our path initialisation method. We show that it achieves significantly higher success rates than the baselines, at a reduced computational burden.Open Acces
Dynamic railway junction rescheduling using population based ant colony optimisation
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Efficient rescheduling after a perturbation is an important concern of the railway industry. Extreme delays can result in large fines for the train company as well as dissatisfied customers. The problem is exacerbated by the fact that it is a dynamic one; more timetabled trains may be arriving as the perturbed trains are waiting to be rescheduled. The new trains may have different priorities to the existing trains and thus the rescheduling problem is a dynamic one that changes over time. The aim of this research is to apply a population-based ant colony optimisation algorithm to address this dynamic railway junction rescheduling problem using a simulator modelled on a real-world junction in the UK railway network. The results are promising: the algorithm performs well, particularly when the dynamic changes are of a high magnitude and frequency
Motion Planning for Manipulation With Heuristic Search
Heuristic searches such as A* search are a popular means of finding least-cost
plans due to their generality, strong theoretical guarantees on completeness
and optimality, simplicity in implementation, and consistent behavior. In
planning for robotic manipulation, however, these techniques are commonly
thought of as impractical due to the high-dimensionality of the planning
problem. As part of this thesis work, we have developed a heuristic
search-based approach to motion planning for manipulation that does deal
effectively with the high-dimensionality of the problem. In this thesis,
I will present the approach together with its theoretical properties and show
how to apply it to single-arm and dual-arm motion planning with upright
constraints on a PR2 robot operating in non-trivial cluttered spaces. Then
I will explain how we extended our approach to manipulation planning for
n-arms with regrasping. In this work, the planner itself makes all of the
discrete decisions, including which arm to use for the pickup and putdown, whether
handoffs are necessary and how the object should be grasped at each step along
the way.
An extensive experimental analysis in both simulation and on a physical PR2
shows that, in terms of runtime, our approach is on par with some of the most
common sampling-based approaches. This includes benchmarking our planning
framework on two domains that we constructed that are common to manufacturing:
pick-and-place of fast moving objects and the autonomous assembly of small
objects. Between these applications, the planner exhibited fast planning times
and the ability to robustly plan paths into and out of tight working
environments that are common to assembly. The closing work of this thesis
includes an exhaustive study of the natural tradeoff that occurs between
planning efficiency versus solution quality for different values of the
heuristic inflation factor. A comparison of the solution quality of our planner
to paths computed by an asymptotically optimal approach given a great deal of
time for path optimization is included as well. Finally, a set of experimental
results are included that show that due to our approach\u27s deterministic
cost-minimization, similar input tends to lead to similarity in the output. This
kind of local consistency is important to the predictability of the robot\u27s
motions and contributes to human-robot safety
Multi-Resolution A*
Heuristic search-based planning techniques are commonly used for motion
planning on discretized spaces. The performance of these algorithms is heavily
affected by the resolution at which the search space is discretized. Typically
a fixed resolution is chosen for a given domain. While a finer resolution
allows for better maneuverability, it significantly increases the size of the
state space, and hence demands more search efforts. On the contrary, a coarser
resolution gives a fast exploratory behavior but compromises on maneuverability
and the completeness of the search. To effectively leverage the advantages of
both high and low resolution discretizations, we propose Multi-Resolution A*
(MRA*) algorithm, that runs multiple weighted-A*(WA*) searches having different
resolution levels simultaneously and combines the strengths of all of them. In
addition to these searches, MRA* uses one anchor search to control expansions
from these searches. We show that MRA* is bounded suboptimal with respect to
the anchor resolution search space and resolution complete. We performed
experiments on several motion planning domains including 2D, 3D grid planning
and 7 DOF manipulation planning and compared our approach with several
search-based and sampling-based baselines
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
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