214 research outputs found
Survey on Motion Planning for Multirotor Aerial Vehicles in Plan-based Control Paradigm
In general, optimal motion planning can be performed both locally and globally. In such a planning, the choice in favour of either local or global planning technique mainly depends on whether the environmental conditions are dynamic or static. Hence, the most adequate choice is to use local planning or local planning alongside global planning. When designing optimal motion planning both local and global, the key metrics to bear in mind are execution time, asymptotic optimality, and quick reaction to dynamic obstacles. Such planning approaches can address the aforesaid target metrics more efficiently compared to other approaches such as path planning followed by smoothing. Thus, the foremost objective of this study is to analyse related literature in order to understand how the motion planning, especially trajectory planning, problem is formulated, when being applied for generating optimal trajectories in real-time for Multirotor Aerial Vehicles, impacts the listed metrics. As a result of the research, the trajectory planning problem was broken down into a set of subproblems, and the lists of methods for addressing each of the problems were identified and described in detail. Subsequently, the most prominent results from 2010 to 2022 were summarized and presented in the form of a timeline
Limited Information Shared Control and its Applications to Large Vehicle Manipulators
Diese Dissertation beschäftigt sich mit der kooperativen Regelung einer mobilen Arbeitsmaschine, welche aus einem Nutzfahrzeug und einem oder mehreren hydraulischen Manipulatoren besteht. Solche Maschinen werden für Aufgaben in der Straßenunterhaltungsaufgaben eingesetzt. Die Arbeitsumgebung des Manipulators ist unstrukturiert, was die Bestimmung einer Referenztrajektorie erschwert oder unmöglich macht. Deshalb wird in dieser Arbeit ein Ansatz vorgeschlagen, welcher nur das Fahrzeug automatisiert, während der menschliche Bediener ein Teil des Systems bleibt und den Manipulator steuert. Eine solche Teilautomatisierung des Gesamtsystems führt zu einer speziellen Klasse von Mensch-Maschine-Interaktionen, welche in der Literatur noch nicht untersucht wurde: Eine kooperative Regelung zwischen zwei Teilsystemen, bei der die Automatisierung keine Informationen von dem vom Menschen gesteuerten Teilsystem hat. Deswegen wird in dieser Arbeit ein systematischer Ansatz der kooperativen Regelung mit begrenzter Information vorgestellt, der den menschlichen Bediener unterstützen kann, ohne die Referenzen oder die Systemzustände des Manipulators zu messen. Außerdem wird ein systematisches Entwurfskonzept für die kooperative Regelung mit begrenzter Information vorgestellt. Für diese Entwurfsmethode werden zwei neue Unterklassen der sogenannten Potenzialspiele eingeführt, die eine systematische Berechnung der Parameter der entwickelten kooperativen Regelung ohne manuelle Abstimmung ermöglichen. Schließlich wird das entwickelte Konzept der kooperativen Regelung am Beispiel einer großen mobilen Arbeitsmaschine angewandt, um seine Vorteile zu ermitteln und zu bewerten. Nach der Analyse in Simulationen wird die praktische Anwendbarkeit der Methode in drei Experimenten mit menschlichen Probanden an einem Simulator untersucht. Die Ergebnisse zeigen die Überlegenheit des entwickelten kooperativen Regelungskonzepts gegenüber der manuellen Steuerung und der nicht-kooperativen Steuerung hinsichtlich sowohl der objektiven Performanz als auch der subjektiven Bewertung der Probanden. Somit zeigt diese Dissertation, dass die kooperative Regelung mobiler Arbeitsmaschinen mit den entwickelten theoretischen Konzepten sowohl hilfreich als auch praktisch anwendbar ist
Undergraduate and Graduate Course Descriptions, 2023 Spring
Wright State University undergraduate and graduate course descriptions from Spring 2023
Resilient planning, task assignment and control for multi-robot systems against plan-deviation attacks
The security of multi-robot systems is critical in various applications such as patrol, transportation, and search and rescue operations, where they face threats from adversaries attempting to gain control of the robots. These compromised robots are significant threats as they allow attackers to steer robots towards forbidden areas without being detected, potentially causing harm or compromising the mission. To address this problem, we propose a resilient planning, task assignment, and control framework. The proposed framework builds a multi-robot plan where robots are designed to get close enough to other robots according to a co-observation schedule, in order to mutually check for abnormal behaviors. For the first part of the thesis, we propose an optimal trajectory solver based on the alternating direction method of multipliers (ADMM) to generate multi-agent trajectories that satisfy spatio-temporal requirements introduced by the co-observation schedules. As part of the formulation, we provide a new reachability constraint to guarantee that, despite adversarial movement by the attacker, a compromised robot cannot reach forbidden areas between co-observations without being detected.
In the second part of the thesis, to further enhance the system's performance, reliability, and robustness, we propose to deploy multiple robots on each route to form sub-teams. A new cross-trajectory co-observation scheme between sub-teams is introduced that preserves the optimal unsecured trajectories. The new planner ensures that at least one robot in each sub-team sticks to the planned trajectories, while sub-teams can constantly exchange robots during the task introducing additional co-observations that can secure originally unsecured routes. We show that the planning of cross-trajectory co-observations can be transformed into a network flow problem and solved using traditional linear program technique. In the final part of the thesis, we show that the introduction of sub-teams also improves the multi-robot system's robustness to unplanned situations, allowing servicing unplanned online events without breaking the security requirements. This is achieved by a distributed task assignment algorithm based on consensus ADMM which can handle tasks with different priorities. The assignment result and security requirements are formulated as spatio-temporal schedules and guaranteed through control barrier function (CBF) based controls
Towards full-scale autonomy for multi-vehicle systems planning and acting in extreme environments
Currently, robotic technology offers flexible platforms for addressing many challenging problems that arise in extreme environments. These problems’ nature enhances
the use of heterogeneous multi-vehicle systems which can coordinate and collaborate
to achieve a common set of goals. While such applications have previously been
explored in limited contexts, long-term deployments in such settings often require
an advanced level of autonomy to maintain operability.
The success of planning and acting approaches for multi-robot systems are conditioned by including reasoning regarding temporal, resource and knowledge requirements, and world dynamics. Automated planning provides the tools to enable intelligent behaviours in robotic systems. However, whilst many planning approaches and
plan execution techniques have been proposed, these solutions highlight an inability
to consistently build and execute high-quality plans.
Motivated by these challenges, this thesis presents developments advancing state-of-the-art temporal planning and acting to address multi-robot problems. We propose a set of advanced techniques, methods and tools to build a high-level temporal
planning and execution system that can devise, execute and monitor plans suitable for long-term missions in extreme environments. We introduce a new task
allocation strategy, called HRTA, that optimises the task distribution amongst the
heterogeneous fleet, relaxes the planning problem and boosts the plan search. We
implement the TraCE planner that enforces contingent planning considering propositional temporal and numeric constraints to deal with partial observability about
the initial state. Our developments regarding robust plan execution and mission
adaptability include the HLMA, which efficiently optimises the task allocation and
refines the planning model considering the experience from robots’ previous mission
executions. We introduce the SEA failure solver that, combined with online planning, overcomes unexpected situations during mission execution, deals with joint
goals implementation, and enhances mission operability in long-term deployments.
Finally, we demonstrate the efficiency of our approaches with a series of experiments
using a new set of real-world planning domains.Engineering and Physical Sciences Research Council (EPSRC) grant EP/R026173/
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
Contributions to deconfliction advanced U-space services for multiple unmanned aerial systems including field tests validation
Unmanned Aerial Systems (UAS) will become commonplace, the number of UAS
flying in European airspace is expected to increase from a few thousand to hundreds
of thousands by 2050. To prepare for this approaching, national and international
organizations involved in aerial traffic management are now developing new laws
and restructuring the airspace to incorporate UAS into civil airspace. The Single
European Sky ATM Research considers the development of the U-space, a crucial
step to enable the safe, secure, and efficient access of a large set of UAS into airspace.
The design, integration, and validation of a set of modules that contribute to our
UTM architecture for advanced U-space services are described in this Thesis. With
an emphasis on conflict detection and resolution features, the architecture is flexible,
modular, and scalable. The UTM is designed to work without the need for human
involvement, to achieve U-space required scalability due to the large number of expected
operations. However, it recommends actions to the UAS operator since, under
current regulations, the operator is accountable for carrying out the recommendations
of the UTM. Moreover, our development is based on the Robot Operating System
(ROS) and is open source.
The main developments of the proposed Thesis are monitoring and tactical deconfliction
services, which are in charge of identifying and resolving possible conflicts
that arise in the shared airspace of several UAS. By limiting the conflict search to a
local search surrounding each waypoint, the proposed conflict detection method aims
to improve conflict detection. By splitting the issue down into smaller subproblems
with only two waypoints, the conflict resolution method tries to decrease the deviation
distance from the initial flight plan. The proposed method for resolving potential threats is based on the premise that
UAS can follow trajectories in time and space properly. Therefore, another contribution
of the presented Thesis is an UAS 4D trajectory follower that can correct space
and temporal deviations while following a given trajectory. Currently, commercial autopilots
do not offer this functionality that allows to improve the airspace occupancy
using time as an additional dimension.
Moreover, the integration of onboard detect and avoid capabilities, as well as the
consequences for U-space services are examined in this Thesis. A module capable
of detecting large static unexpected obstacles and generating an alternative route to
avoid the obstacle online is presented.
Finally, the presented UTM architecture has been tested in both software-in-theloop
and hardware-in-the-loop development enviroments, but also in real scenarios
using unmanned aircraft. These scenarios were designed by selecting the most relevant
UAS operation applications, such as the inspection of wind turbines, power lines
and precision agriculture, as well as event and forest monitoring. ATLAS and El
Arenosillo were the locations of the tests carried out thanks to the European projects
SAFEDRONE and GAUSS.Los sistemas aéreos no tripulados (UAS en inglés) se convertirán en algo habitual. Se prevé que el
número de UAS que vuelen en el espacio aéreo europeo pase de unos pocos miles a cientos de
miles en 2050. Para prepararse para esta aproximación, las organizaciones nacionales e
internacionales dedicadas a la gestión del tráfico aéreo están elaborando nuevas leyes y
reestructurando el espacio aéreo para incorporar los UAS al espacio aéreo civil. SESAR (del inglés
Single European Sky ATM Research) considera el desarrollo de U-space, un paso crucial para
permitir el acceso seguro y eficiente de un gran conjunto de UAS al espacio aéreo.
En esta Tesis se describe el diseño, la integración y la validación de un conjunto de módulos que
contribuyen a nuestra arquitectura UTM (del inglés Unmanned aerial system Traffic Management)
para los servicios avanzados del U-space. Con un énfasis en las características de detección y
resolución de conflictos, la arquitectura es flexible, modular y escalable. La UTM está diseñada para
funcionar sin necesidad de intervención humana, para lograr la escalabilidad requerida por U-space
debido al gran número de operaciones previstas. Sin embargo, la UTM únicamente recomienda
acciones al operador del UAS ya que, según la normativa vigente, el operador es responsable de las
operaciones realizadas. Además, nuestro desarrollo está basado en el Sistema Operativo de Robots
(ROS en inglés) y es de código abierto.
Los principales desarrollos de la presente Tesis son los servicios de monitorización y evitación de
conflictos, que se encargan de identificar y resolver los posibles conflictos que surjan en el espacio
aéreo compartido de varios UAS. Limitando la búsqueda de conflictos a una búsqueda local
alrededor de cada punto de ruta, el método de detección de conflictos pretende mejorar la detección
de conflictos. Al dividir el problema en subproblemas más pequeños con sólo dos puntos de ruta, el
método de resolución de conflictos intenta disminuir la distancia de desviación del plan de vuelo
inicial.
El método de resolución de conflictos propuesto se basa en la premisa de que los UAS pueden
seguir las trayectorias en el tiempo y espacio de forma adecuada. Por tanto, otra de las aportaciones
de la Tesis presentada es un seguidor de trayectorias 4D de UAS que puede corregir las
desviaciones espaciales y temporales mientras sigue una trayectoria determinada. Actualmente, los
autopilotos comerciales no ofrecen esta funcionalidad que permite mejorar la ocupación del espacio
aéreo utilizando el tiempo como una dimensión adicional.
Además, en esta Tesis se examina la capacidad de integración de módulos a bordo de detección y
evitación de obstáculos, así como las consecuencias para los servicios de U-space. Se presenta un
módulo capaz de detectar grandes obstáculos estáticos inesperados y capaz de generar una ruta
alternativa para evitar dicho obstáculo.
Por último, la arquitectura UTM presentada ha sido probada en entornos de desarrollo de simulación,
pero también en escenarios reales con aeronaves no tripuladas. Estos escenarios se diseñaron
seleccionando las aplicaciones de operación de UAS más relevantes, como la inspección de
aerogeneradores, líneas eléctricas y agricultura de precisión, así como la monitorización de eventos y
bosques. ATLAS y El Arenosillo fueron las sedes de las pruebas realizadas gracias a los proyectos
europeos SAFEDRONE y GAUSS
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