1,073 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 IngenierĂa Telemática por la Universidad Carlos III de MadridPresidente: Karl Holger.- Secretario: Joerg Widmer.- Vocal: Claudio Cicconett
Towards adaptive multi-robot systems: self-organization and self-adaptation
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible
On the Dynamic Evolution of Distributed Computational Aggregates
Engineering and programming approaches for collective adaptive systems often leverage ensemble-like abstractions
to characterise a subset of devices as a domain for a given
task or computation. In this paper, we address the problem of
programming the dynamic evolution of distributed computational
aggregates, through neighbour-based coordination. This is a
problem of interest, since several situated activities (especially
in large-scale settings) require decentralised collaboration, and
need to be sustained by limited subsets of devices. These subsets
may vary dynamically due to delegation, completion of local
contributions, exhaustion of resources, failure, or change in the
device set induced by the openness of system boundaries.
In order to study and develop how distributed aggregates
progressively take form by local coordination, we build on the
field-based framework of aggregate processes, and extend it
with techniques to support more expressive evolution dynamics.
We propose novel algorithms for more effective propagation
and closure of the boundaries of dynamic aggregates, based on
statistics on the information speed and a notion of progressive
closure through wave-like propagation. We verify the proposed
techniques by simulation of a paradigmatic case study of multihop message delivery in mobile settings, and show increased
performance and success rate with respect to previous work
Motion Planning of UAV Swarm: Recent Challenges and Approaches
The unmanned aerial vehicle (UAV) swarm is gaining massive interest for researchers as it has huge significance over a single UAV. Many studies focus only on a few challenges of this complex multidisciplinary group. Most of them have certain limitations. This paper aims to recognize and arrange relevant research for evaluating motion planning techniques and models for a swarm from the viewpoint of control, path planning, architecture, communication, monitoring and tracking, and safety issues. Then, a state-of-the-art understanding of the UAV swarm and an overview of swarm intelligence (SI) are provided in this research. Multiple challenges are considered, and some approaches are presented. Findings show that swarm intelligence is leading in this era and is the most significant approach for UAV swarm that offers distinct contributions in different environments. This integration of studies will serve as a basis for knowledge concerning swarm, create guidelines for motion planning issues, and strengthens support for existing methods. Moreover, this paper possesses the capacity to engender new strategies that can serve as the grounds for future work
Algorithms for Modular Self-reconfigurable Robots: Decision Making, Planning, and Learning
Modular self-reconfigurable robots (MSRs) are composed of multiple robotic modules which can change their connections with each other to take different shapes, commonly known as configurations. Forming different configurations helps the MSR to accomplish different types of tasks in different environments. In this dissertation, we study three different problems in MSRs: partitioning of modules, configuration formation planning and locomotion learning, and we propose algorithmic solutions to solve these problems.
Partitioning of modules is a decision-making problem for MSRs where each module decides which partition or team of modules it should be in. To find the best set of partitions is a NP-complete problem. We propose game theory based both centralized and distributed solutions to solve this problem. Once the modules know which set of modules they should team-up with, they self-aggregate to form a specific shaped configuration, known as the configuration formation planning problem. Modules can be either singletons or connected in smaller configurations from which they need to form the target configuration. The configuration formation problem is difficult as multiple modules may select the same location in the target configuration to move to which might result in occlusion and consequently failure of the configuration formation process. On the other hand, if the modules are already in connected configurations in the beginning, then it would be beneficial to preserve those initial configurations for placing them into the target configuration as disconnections and re-connections are costly operations. We propose solutions based on an auction-like algorithm and (sub) graph-isomorphism technique to solve the configuration formation problem.
Once the configuration is built, the MSR needs to move towards its goal location as a whole configuration for completing its task. If the configuration’s shape and size is not known a priori, then planning its locomotion is a difficult task as it needs to learn the locomotion pattern in dynamic time – the problem is known as adaptive locomotion learning. We have proposed reinforcement learning based fault-tolerant solutions for locomotion learning by MSRs
Robust Wireless Sensor Network Deployment
International audienceIn this work we present a decentralized deployment algorithm for wireless mobile sensor networks focused on deployment Efficiency, connectivity Maintenance and network Reparation (EMR). We assume that a group of mobile sensors is placed in the area of interest to be covered, without any prior knowledge of the environment. The goal of the algorithm is to maximize the covered area and cope with sudden sensor failures. By relying on the locally available information regarding the environment and neighborhood, and without the need for any kind of synchronization in the network, each sensor iteratively chooses the next-step movement location so as to form a hexagonal lattice grid. Relying on the graph of wireless mobile sensors, we are able to provide the properties regarding the quality of coverage, the connectivity of the graph and the termination of the algorithm. We run extensive simulations to provide compactness properties of the deployment and evaluate the robustness against sensor failures. We show through the analysis and the simulations that EMR algorithm is robust to node failures and can restore the lattice grid. We also show that even after a failure, EMR algorithm call still provide a compact deployment in a reasonable time
Multi Agent Systems
Research on multi-agent systems is enlarging our future technical capabilities as humans and as an intelligent society. During recent years many effective applications have been implemented and are part of our daily life. These applications have agent-based models and methods as an important ingredient. Markets, finance world, robotics, medical technology, social negotiation, video games, big-data science, etc. are some of the branches where the knowledge gained through multi-agent simulations is necessary and where new software engineering tools are continuously created and tested in order to reach an effective technology transfer to impact our lives. This book brings together researchers working in several fields that cover the techniques, the challenges and the applications of multi-agent systems in a wide variety of aspects related to learning algorithms for different devices such as vehicles, robots and drones, computational optimization to reach a more efficient energy distribution in power grids and the use of social networks and decision strategies applied to the smart learning and education environments in emergent countries. We hope that this book can be useful and become a guide or reference to an audience interested in the developments and applications of multi-agent systems
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