83 research outputs found
On the motion planning & control of nonlinear robotic systems
In the last decades, we saw a soaring interest in autonomous robots boosted not only by academia and industry, but also by the ever in- creasing demand from civil users. As a matter of fact, autonomous robots are fast spreading in all aspects of human life, we can see them clean houses, navigate through city traffic, or harvest fruits and vegetables. Almost all commercial drones already exhibit unprecedented and sophisticated skills which makes them suitable for these applications, such as obstacle avoidance, simultaneous localisation and mapping, path planning, visual-inertial odometry, and object tracking. The major limitations of such robotic platforms lie in the limited payload that can carry, in their costs, and in the limited autonomy due to finite battery capability. For this reason researchers start to develop new algorithms able to run even on resource constrained platforms both in terms of computation capabilities and limited types of endowed sensors, focusing especially on very cheap sensors and hardware. The possibility to use a limited number of sensors allowed to scale a lot the UAVs size, while the implementation of new efficient algorithms, performing the same task in lower time, allows for lower autonomy. However, the developed robots are not mature enough to completely operate autonomously without human supervision due to still too big dimensions (especially for aerial vehicles), which make these platforms unsafe for humans, and the high probability of numerical, and decision, errors that robots may make. In this perspective, this thesis aims to review and improve the current state-of-the-art solutions for autonomous navigation from a purely practical point of view. In particular, we deeply focused on the problems of robot control, trajectory planning, environments exploration, and obstacle avoidance
A review of task allocation methods for UAVs
Unmanned aerial vehicles, can offer solutions to a lot of problems, making it crucial to research more and improve the task allocation methods used. In this survey, the main approaches used for task allocation in applications involving UAVs are presented as well as the most common applications of UAVs that require the application of task allocation methods. They are followed by the categories of the task allocation algorithms used, with the main focus being on more recent works. Our analysis of these methods focuses primarily on their complexity, optimality, and scalability. Additionally, the communication schemes commonly utilized are presented, as well as the impact of uncertainty on task allocation of UAVs. Finally, these methods are compared based on the aforementioned criteria, suggesting the most promising approaches
Human–agent team dynamics: a review and future research opportunities
Humans teaming with intelligent autonomous agents is becoming indispensable in work environments. However, human–agent teams pose significant challenges, as team dynamics are complex arising from the task and social aspects of human–agent interactions. To improve our understanding of human–agent team dynamics, in this article, we conduct a systematic literature review. Drawing on Mathieu et al.’s (2019) teamwork model developed for all-human teams, we map the landscape of research to human–agent team dynamics, including structural features, compositional features, mediating mechanisms, and the interplay of the above features and mechanisms. We reveal that the development of human–agent team dynamics is still nascent, with a particular focus on information sharing, trust development, agents’ human likeness behaviors, shared cognitions, situation awareness, and function allocation. Gaps remain in many areas of team dynamics, such as team processes, adaptability, shared leadership, and team diversity. We offer various interdisciplinary pathways to advance research on human–agent teams
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/
Graph-based Decentralized Task Allocation for Multi-Robot Target Localization
We introduce a new approach to address the task allocation problem in a
system of heterogeneous robots comprising of Unmanned Ground Vehicles (UGVs)
and Unmanned Aerial Vehicles (UAVs). The proposed model, \texttt{\method}, or
\textbf{G}raph \textbf{A}ttention \textbf{T}ask \textbf{A}llocato\textbf{R}
aggregates information from neighbors in the multi-robot system, with the aim
of achieving joint optimality in the target localization efficiency.Being
decentralized, our method is highly robust and adaptable to situations where
collaborators may change over time, ensuring the continuity of the mission. We
also proposed heterogeneity-aware preprocessing to let all the different types
of robots collaborate with a uniform model.The experimental results demonstrate
the effectiveness and scalability of the proposed approach in a range of
simulated scenarios. The model can allocate targets' positions close to the
expert algorithm's result, with a median spatial gap less than a unit length.
This approach can be used in multi-robot systems deployed in search and rescue
missions, environmental monitoring, and disaster response
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
Route Planning and Operator Allocation in Robot Fleets
In this thesis, we address various challenges related to optimal planning and task allocation in a robot fleet supervised by remote human operators. The overarching goal is to enhance the performance and efficiency of the robot fleets by planning routes and scheduling operator assistance while accounting for limited human availability. The thesis consists of three main problems, each of which focuses on a specific aspect of the system.
The first problem pertains to optimal planning for a robot in a collaborative human-robot team, where the human supervisor is intermittently available to assist the robot to complete its tasks faster. Specifically, we address the challenge of computing the fastest route between two configurations in an environment with time constraints on how long the robot can wait for assistance at intermediate configurations. We consider the application of robot navigation in a city environment, where different routes can have distinct speed limits and different time constraints on how long a robot is allowed to wait. Our proposed approach utilizes the concepts of budget and critical departure times, enabling optimal solution and enhanced scalability compared to existing methods. Extensive comparisons with baseline algorithms on a city road network demonstrate its effectiveness and ability to achieve high-quality solutions. Furthermore, we extend the problem to the multi-robot case, where the challenge lies in prioritizing robots when multiple service requests arrive simultaneously. To address this challenge, we present a greedy algorithm that efficiently prioritizes service requests in a batch and has a remarkably good performance compared to the optimal solution.
The next problem focuses on allocating human operators to robots in a fleet, considering each robot's specified route and the potential for failures and getting stuck. Conventional techniques used to solve such problems face scalability issues due to exponential growth of state and action spaces with the number of robots and operators. To overcome these, we derive conditions for a technical requirement called indexability, thereby enabling the use of the Whittle index heuristic. Our key insight is to leverage the structure of the value function of individual robots, resulting in conditions that can be easily verified separately for each state of each robot. We apply these conditions to two types of transitions commonly seen in supervised robot fleets. Through numerical simulations, we demonstrate the efficacy of Whittle index policy as a near-optimal scalable approach that outperforms existing scalable methods.
Finally, we investigate the impact of interruptions on human supervisors overseeing a fleet of robots. Human supervisors in such systems are primarily responsible for monitoring robots, but can also be assigned with secondary tasks. These tasks can act as interruptions and can be categorized as either intrinsic, i.e., being directly related to the monitoring task, or extrinsic, i.e., being unrelated. Through a user study involving participants, the findings reveal that task performance remains relatively unaffected by interruptions, and is primarily dependent on the number of robots being monitored. However, extrinsic interruptions led to a significant increase in perceived workload, creating challenges in switching between tasks. These results highlight the importance of managing user workload by limiting extrinsic interruptions in such supervision systems.
Overall, this thesis contributes to the field of robot planning and operator allocation in collaborative human-robot teams. By incorporating human assistance, addressing scalability challenges, and understanding the impact of interruptions, we aim to enhance the performance and usability of robot fleets. Our work introduces optimal planning methods and efficient allocation strategies, empowering the seamless operation of robot fleets in real-world scenarios. Additionally, we provide valuable insights into user workload, shedding light on the interactions between humans and robots in such systems. We hope that our research promotes the widespread adoption of robot fleets and facilitates their integration into various domains, ultimately driving advancements in the field
Swarm-Based Drone-as-a-Service for Delivery
There has been a growing interest in the applications of drones as a cost-effective, efficient, and environmentally friendly alternative in various domains. Particularly in the context of delivery services, the demand for contactless and efficient delivery solutions has surged. Drone delivery offers faster and greener deliveries. However, existing methods focus primarily on point-to-point delivery, limiting their potential for optimisation. This thesis proposes a novel approach to servitise drone delivery by operating through a skyway network composed of building rooftops, enabling drones to traverse between source and destination while recharging at intermediate nodes. Although single drone delivery offers numerous advantages, it faces significant challenges in scenarios where multiple packages require simultaneous delivery. Flight regulations, which often limit the carrying capacity of individual drones, necessitate the exploration of alternative solutions. Therefore, this thesis presents a novel Swarm-Based Drone-as-a-Service (SDaaS) model and framework for multiple package delivery. The proposed framework prioritises the composition of services that optimise Quality of Service (QoS) factors, such as delivery time and energy consumption. This thesis identifies swarm-specific constraints and leverages the unique characteristics of drone swarms. It explores swarm formations, in-flight wireless charging between drones, and allocation problems to maximise drone utilisation for consumer deliveries. Furthermore, this research investigates the recommendation of services to consumers based on their preferences, aiming to increase their satisfaction. Moreover, the framework addresses the resilience of SDaaS by addressing issues related to drone soft failures and their impact on other swarm members. Ultimately, this work paves the way for the widespread adoption and optimisation of swarm-based drone services in the context of last-mile delivery
Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico
Conference proceedings info:
ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies
Raleigh, HI, United States, March 24-26, 2023
Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center
of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologías de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clínicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la Secretaría de Salud, el Centro de Comando, Comunicaciones y Control Informático.
de la Secretaría del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-
Autonomous Navigation of Distributed Spacecraft using Graph-based SLAM for Proximity Operations in Small Celestial Bodies
Establishment of a sustainable human presence beyond the cislunar space is a major milestone for mankind. Small celestial bodies (SCBs) like asteroids are known to contain valuable natural resources necessary for the development of space assets essential to the accomplishment of this goal. Consequently, future robotic spacecraft missions to SCBs are envisioned with the objective of commercial in-situ resource utilization (ISRU). In mission design, there is also an increasing interest in the utilization of the distributed spacecraft, to benefit from specialization and redundancy. The ability of distributed spacecraft to navigate autonomously in the proximity of a SCB is indispensable for the successful realization of ISRU mission objectives. Quasi-autonomous methods currently used for proximity navigation require extensive ground support for mapping and model development, which can be an impediment for large scale multi-spacecraft ISRU missions in the future.
It is prudent to leverage the advances in terrestrial robotic navigation to investigate the development of novel methods for autonomous navigation of spacecraft. The primary objective of the work presented in this thesis is to evaluate the feasibility and investigate the development of methods based on graph-based simultaneous localization and mapping (SLAM), a popular algorithm used in terrestrial autonomous navigation, for the autonomous navigation of distributed spacecraft in the proximity of SCBs. To this end, recent research in graph-based SLAM is extensively studied to identify strategies used to enable multi-agent navigation. The spacecraft navigation requirement is formulated as a graph-based SLAM problem using metric GraphSLAM or topometric graph-based SLAM. Techniques developed based on the identified strategies namely, map merging, inter-spacecraft measurements and relative localization are then applied to this formulation to enable distributed spacecraft navigation. In each case, navigation is formulated in terms of its application to a proximity operation scenario that best suits the multi-agent navigation technique.
Several challenges related to the application of graph-based SLAM for spacecraft navigation, such as computational cost and illumination variation are also identified and addressed in the development of these methods. Experiments are performed using simulated models of asteroids and spacecraft dynamics, comparing the estimated states of the spacecraft and landmarks to the assumed true states. The results from the experiments indicate a consistent and robust state determination process, suggesting the suitability of the application of multi-agent navigation techniques to graph-based SLAM for enabling the autonomous navigation of distributed spacecraft near SCBs
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