26 research outputs found
Towards edge robotics: the progress from cloud-based robotic systems to intelligent and context-aware robotic services
Current robotic systems handle a different range of applications such as video surveillance, delivery
of goods, cleaning, material handling, assembly, painting, or pick and place services. These systems
have been embraced not only by the general population but also by the vertical industries to
help them in performing daily activities. Traditionally, the robotic systems have been deployed in
standalone robots that were exclusively dedicated to performing a specific task such as cleaning the
floor in indoor environments. In recent years, cloud providers started to offer their infrastructures
to robotic systems for offloading some of the robot’s functions. This ultimate form of the distributed
robotic system was first introduced 10 years ago as cloud robotics and nowadays a lot of robotic solutions
are appearing in this form. As a result, standalone robots became software-enhanced objects
with increased reconfigurability as well as decreased complexity and cost. Moreover, by offloading
the heavy processing from the robot to the cloud, it is easier to share services and information from
various robots or agents to achieve better cooperation and coordination.
Cloud robotics is suitable for human-scale responsive and delay-tolerant robotic functionalities
(e.g., monitoring, predictive maintenance). However, there is a whole set of real-time robotic applications
(e.g., remote control, motion planning, autonomous navigation) that can not be executed with
cloud robotics solutions, mainly because cloud facilities traditionally reside far away from the robots.
While the cloud providers can ensure certain performance in their infrastructure, very little can be
ensured in the network between the robots and the cloud, especially in the last hop where wireless
radio access networks are involved. Over the last years advances in edge computing, fog computing,
5G NR, network slicing, Network Function Virtualization (NFV), and network orchestration are stimulating
the interest of the industrial sector to satisfy the stringent and real-time requirements of their
applications. Robotic systems are a key piece in the industrial digital transformation and their benefits
are very well studied in the literature. However, designing and implementing a robotic system
that integrates all the emerging technologies and meets the connectivity requirements (e.g., latency,
reliability) is an ambitious task.
This thesis studies the integration of modern Information andCommunication Technologies (ICTs)
in robotic systems and proposes some robotic enhancements that tackle the real-time constraints of
robotic services. To evaluate the performance of the proposed enhancements, this thesis departs
from the design and prototype implementation of an edge native robotic system that embodies the concepts of edge computing, fog computing, orchestration, and virtualization. The proposed edge
robotics system serves to represent two exemplary robotic applications. In particular, autonomous
navigation of mobile robots and remote-control of robot manipulator where the end-to-end robotic
system is distributed between the robots and the edge server. The open-source prototype implementation
of the designed edge native robotic system resulted in the creation of two real-world testbeds
that are used in this thesis as a baseline scenario for the evaluation of new innovative solutions in
robotic systems.
After detailing the design and prototype implementation of the end-to-end edge native robotic
system, this thesis proposes several enhancements that can be offered to robotic systems by adapting
the concept of edge computing via the Multi-Access Edge Computing (MEC) framework. First, it
proposes exemplary network context-aware enhancements in which the real-time information about
robot connectivity and location can be used to dynamically adapt the end-to-end system behavior to
the actual status of the communication (e.g., radio channel). Three different exemplary context-aware
enhancements are proposed that aim to optimize the end-to-end edge native robotic system. Later,
the thesis studies the capability of the edge native robotic system to offer potential savings by means of
computation offloading for robot manipulators in different deployment configurations. Further, the
impact of different wireless channels (e.g., 5G, 4G andWi-Fi) to support the data exchange between a
robot manipulator and its remote controller are assessed.
In the following part of the thesis, the focus is set on how orchestration solutions can support
mobile robot systems to make high quality decisions. The application of OKpi as an orchestration algorithm
and DLT-based federation are studied to meet the KPIs that autonomously controlledmobile
robots have in order to provide uninterrupted connectivity over the radio access network. The elaborated
solutions present high compatibility with the designed edge robotics system where the robot
driving range is extended without any interruption of the end-to-end edge robotics service. While the
DLT-based federation extends the robot driving range by deploying access point extension on top of
external domain infrastructure, OKpi selects the most suitable access point and computing resource
in the cloud-to-thing continuum in order to fulfill the latency requirements of autonomously controlled
mobile robots.
To conclude the thesis the focus is set on how robotic systems can improve their performance by
leveraging Artificial Intelligence (AI) and Machine Learning (ML) algorithms to generate smart decisions.
To do so, the edge native robotic system is presented as a true embodiment of a Cyber-Physical
System (CPS) in Industry 4.0, showing the mission of AI in such concept. It presents the key enabling
technologies of the edge robotic system such as edge, fog, and 5G, where the physical processes are
integrated with computing and network domains. The role of AI in each technology domain is identified
by analyzing a set of AI agents at the application and infrastructure level. In the last part of the
thesis, the movement prediction is selected to study the feasibility of applying a forecast-based recovery
mechanism for real-time remote control of robotic manipulators (FoReCo) that uses ML to infer
lost commands caused by interference in the wireless channel. The obtained results are showcasing
the its potential in simulation and real-world experimentation.Programa de Doctorado en IngenierÃa Telemática por la Universidad Carlos III de MadridPresidente: Karl Holger.- Secretario: Joerg Widmer.- Vocal: Claudio Cicconett
Resource Requirements of an Edge-based Digital Twin Service: An Experimental Study 
Digital Twin (DT) is a pivotal application under the industrial digital transformation envisaged
by the fourth industrial revolution (Industry 4.0). DT defines intelligent and real-time faithful reflections of
physical entities such as industrial robots, thus allowing their remote control. Relying on the latest advances
in Information and Communication Technologies (ICT), namely Network Function Virtualization (NFV) and
Edge-computing, DT can be deployed as an on-demand service in the factories close proximity and offered
leveraging radio access technologies. However, with the purpose of achieving the well-known scalability,
flexibility, availability and performance guarantees benefits foreseen by the latest ICT, it is steadily required
to experimentally profile and assess DT as a Service (DTaaS) solutions. Moreover, the dependencies between
the resources claimed by the service and the relative demand and work loads require to be investigated.
In this work, an Edge-based Digital Twin solution for remote control of robotic arms is deployed in an
experimental testbed where, in compliance with the NFV paradigm, the service has been segmented in virtual
network functions. Our research has primarily the objective to evaluate the entanglement among overall
service performance and VNFs resource requirements, and the number of robots consuming the service
varies. Experimental profiles show the most critical DT features to be the inverse kinematics and trajectory
computations. Moreover, the same analysis has been carried out as a function of the industrial processes,
namely based on the commands imposed on the robots, and particularly of their abstraction-level, resulting
in a novel trade-off between computing and time resources requirements and trajectory guarantees. The
derived results provide crucial insights for the design of network service scaling and resource orchestration
frameworks dealing with DTaaS applications. Finally, we empirically prove LTE shortage to accommodate
the minimum DT latency requirements
KPI guarantees in network slicing
Thanks to network slicing, mobile networks can now support multiple and diverse services, each requiring different key performance indicators (KPIs). In this new scenario, it is critical to allocate network and computing resources efficiently and in such a way that all KPIs targeted by a service are met. Accounting for all sorts of KPIs (e.g., availability and reliability, besides the more traditional throughput and latency) is an aspect that has been scarcely addressed so far and that requires tailored models and solution strategies. We address this issue by proposing a novel methodology and resource orchestration scheme, named OKpi, which provides high-quality decisions on VNF (Virtual Network Function) placement and data routing, including the selection of radio points of attachment. Importantly, OKpi has polynomial computational complexity and accounts for all KPIs required by each service, and for any resource available from the fog to the cloud. We prove several properties of OKpi and demonstrate that it performs very closely to the optimum under real-world scenarios. We also implement OKpi in a testbed supporting a robot-based, smart factory service, and we present some field tests that further confirm the ability of OKpi to make high-quality decisions.This work was supported by the EU Commission through the 5Growth Project under Agreement 856709
waveSLAM: Empowering accurate indoor mapping using off-the-shelf millimeter-wave self-sensing
Proceedings of: 2023 IEEE 98th Vehicular Technology Conference: VTC2023-Fall, 10-13 October 2023, Hong Kong.This paper presents the design, implementation and evaluation of waveSLAM, a low-cost mobile robot system that uses the millimetre wave (mmWave) communication devices to enhance the indoor mapping process targeting environments with reduced visibility or glass/mirror walls. A unique feature of waveSLAM is that it only leverages existing Commercial-Off-The-Shelf (COTS) hardware (Lidar and mmWave radios) that are mounted on mobile robots to improve the accurate indoor mapping achieved with optical sensors. The key intuition behind the waveSLAM design is that while the mobile robots moves freely, the mmWave radios can periodically exchange angle and distance estimates between themselves (self-sensing) by bouncing the signal from the environment, thus enabling accurate estimates of the target object/material surface. Our experiments verify that waveSLAM can archive cm-level accuracy with errors below 22 cm and 20◦ in angle orientation which is compatible with Lidar when building indoor maps.This work has been partially funded by the European Union's Horizon Europe research and innovation program under grant agreement No 101095759 (Hexa-X-II) and the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union-Next Generation EU through the UNICO 5G I+D 6G-EDGEDT
Enhancing Edge robotics through the use of context information
Cloud robotics aims at endowing robot systems with powerful capabilities by leveraging the computing resources available in theCloud. To that end, the Cloud infrastructure consolidates servicesand information among the robots, enabling a degree of centralization which has the potential to improve operations. Despite beingvery promising, Cloud robotics presents two critical issues: (i) it isvery hard to control the network between the robots and the Cloud(e.g., long delays, high jitter), and (ii) local context information (e.g.,on the access network) is not available in the Cloud. This makeshard to achieve deterministic performance for robotics applications.Over the last few years, Edge computing has emerged as a trend toprovide services and computing capabilities directly in the accessnetwork. This is so because of the additional benefits enabled byEdge computing: (i) it is easier to control the network end-to-end,and (ii) local context information (e.g., about the wireless channel) can be made available for use by applications. The goal of this paperis to showcase, by means of real-life experimentation, the benefits ofresiding at the Edge for robotics applications, due to the possibilityof consuming context information locally available. In our experimentation, an application running in the Edge controls over a Wi-Filink the movement of a robot. Information related to the wirelesschannel is made available via a service at the Edge, which is thenconsumed by the application.Results show that a smoother drivingof the robot can be achieved when wireless quality information isconsidered as input of the movement control algorithm.This article has been partially supported by the EU H2020 5G-CORAL Project (grant
no. 761586) and by the 5G-City project (grant no. TEC2016-76795-C6-3-R) funded by
the Spanish Ministry of Economy and Competitiveness
Edge Robotics: are we ready? An experimental evaluation of current vision and future directions
Cloud-based robotics systems leverage a wide range of Information Technologies (IT) to offer tangible benefits like cost reduction, powerful computational capabilities, data offloading, etc. However, the centralized nature of cloud computing is not well-suited for a multitude of Operational Technologies (OT) nowadays used in robotics systems that require strict real-time guarantees and security. Edge computing and fog computing are complementary approaches that aim at mitigating some of these challenges by providing computing capabilities closer to the users. The goal of this work is hence threefold: i) to analyze the current edge computing and fog computing landscape in the context of robotics systems, ii) to experimentally evaluate an end-to-end robotics system based on solutions proposed in the literature, and iii) to experimentally identify current benefits and open challenges of edge computing and fog computing. Results show that, in the case of an exemplary delivery application comprising two mobile robots, the robot coordination and range can be improved by consuming real-time radio information available at the edge. However, our evaluation highlights that the existing software, wireless and virtualization technologies still require substantial evolution to fully support edge-based robotics systems.This work has been partially funded by European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 101015956,
and the Spanish Ministry of Economic Affairs and
Digital Transformation and the European Union-
NextGenerationEU through the UNICO 5G I+ D 6G-EDGEDT
and 6G-DATADRIVE
Managing the far-Edge: are today's centralized solutions a good fit
Edge computing has established itself as the foundation for next-generation mobile networks, IT infrastructure, and industrial systems thanks to promised low network latency, computation offloading, and data locality. These properties empower key use-cases like Industry 4.0, Vehicular Communication and Internet of Things. Nowadays implementation of Edge computing is based on extensions to available Cloud computing software tools. While this approach accelerates adoption, it hinders the deployment of the aforementioned use-cases that requires an infrastructure largely more decentralized than Cloud data centers, notably in the far-Edge of the network. In this context, this work aims at: (i) to analyze the differences between Cloud and Edge infrastructures, (ii) to analyze the architecture adopted by the most prominent open-source Edge computing solutions, and (iii) to experimentally evaluate those solutions in terms of scalability and service instantiation time in a medium-size far Edge system. Results show that mainstream Edge solutions require powerful centralized controllers and always-on connectivity, making them unsuitable for highly decentralized scenarios in the far-Edge where stable and high-bandwidth links are not ubiquitous.This work has been partially funded by the H2020 collaborative Europe/Taiwan research project 5G-DIVE (grant no. 589881) and by the H2020 European collaborative research project DAEMON (grant no. 101017109)
Dissecting the impact of information and communication technologies on digital twins as a service
Recent advances on Edge computing, Network Function Virtualization (NFV) and 5G are stimulating the interest of the industrial sector to satisfy the stringent and real-time requirements of their applications. Digital Twin is a key piece in the industrial digital transformation and its benefits are very well studied in the literature. However, designing and implementing a Digital Twin system that integrates all the emerging technologies and meets the connectivity requirements (e.g., latency, reliability) is an ambitious task. Therefore, prototyping the system is required to gradually validate and optimize Digital Twin solutions. In this work, an Edge Robotics Digital Twin system is implemented as a prototype that embodies the concept of Digital Twin as a Service (DTaaS). Such system enables real-time applications such as visualization and remote control, requiring low-latency and high reliability. The capability of the system to offer potential savings by means of computation offloading are analyzed in different deployment configurations. Moreover, the impact of different wireless channels (e.g., 5G, 4G and WiFi) to support the data exchange between a physical device and its virtual components are assessed within operational Digital Twins. Results show that potentially 16% of CPU and 34% of MEM savings can be achieved by virtualizing and offloading software components in the Edge. In addition, they show that 5G connectivity enables remote control of 20 ms, appearing as the most promising radio access technology to support the main requirements of Digital Twin systems.This work was supported in part by the H2020 European Union/Taiwan (EU/TW) Joint Action 5G-eDge Intelligence for Vertical Experimentation (DIVE) under Grant 859881, in part by the H2020 5Growth Project under Grant 856709, in part by the Madrid Government (Comunidad de Madrid-Spain) through the Multiannual Agreement with Universidad Carlos III de Madrid (UC3M) in the line of Excellence of University Professors under Grant EPUC3M21, and in part by the context of the V PRICIT (Regional Program of Research and Technological Innovation)
waveSLAM: Empowering Accurate Indoor Mapping Using Off-the-Shelf Millimeter-wave Self-sensing
This paper presents the design, implementation and evaluation of waveSLAM, a
low-cost mobile robot system that uses the millimetre wave (mmWave)
communication devices to enhance the indoor mapping process targeting
environments with reduced visibility or glass/mirror walls. A unique feature of
waveSLAM is that it only leverages existing Commercial-Off-The-Shelf (COTS)
hardware (Lidar and mmWave radios) that are mounted on mobile robots to improve
the accurate indoor mapping achieved with optical sensors. The key intuition
behind the waveSLAM design is that while the mobile robots moves freely, the
mmWave radios can periodically exchange angle and distance estimates between
themselves (self-sensing) by bouncing the signal from the environment, thus
enabling accurate estimates of the target object/material surface. Our
experiments verify that waveSLAM can archive cm-level accuracy with errors
below 22 cm and 20deg in angle orientation which is compatible with Lidar when
building indoor maps
Don't Let Me Down! Offloading Robot VFs Up to the Cloud
Recent trends in robotic services propose offloading robot functionalities to
the Edge to meet the strict latency requirements of networked robotics.
However, the Edge is typically an expensive resource and sometimes the Cloud is
also an option, thus, decreasing the cost. Following this idea, we propose
Don't Let Me Down! (DLMD), an algorithm that promotes offloading robot
functions to the Cloud when possible to minimize the consumption of Edge
resources. Additionally, DLMD takes the appropriate migration, traffic
steering, and radio handover decisions to meet robotic service requirements as
strict latency constraints. In the paper, we formulate the optimization problem
that DLMD aims to solve, compare DLMD performance against state of art, and
perform stress tests to assess DLMD performance in small & large networks.
Results show that DLMD (i) always finds solutions in less than 30ms; (ii) is
optimal in a local warehousing use case, and (iii) consumes only 5% of the Edge
resources upon network stress.Comment: 5 Pages, 6 figures, submitted to 2023 IEEE 9th International
Conference on Network Softwarization (NetSoft