846 research outputs found
Consensus-based Resource Scheduling for Collaborative Multi-Robot Tasks
We propose integrating the edge-computing paradigm into the multi-robot
collaborative scheduling to maximize resource utilization for complex
collaborative tasks, which many robots must perform together. Examples include
collaborative map-merging to produce a live global map during exploration
instead of traditional approaches that schedule tasks on centralized
cloud-based systems to facilitate computing. Our decentralized approach to a
consensus-based scheduling strategy benefits a multi-robot-edge collaboration
system by adapting to dynamic computation needs and communication-changing
statistics as the system tries to optimize resources while maintaining overall
performance objectives. Before collaborative task offloading, continuous
device, and network profiling are performed at the computing resources, and the
distributed scheduling scheme then selects the resource with maximum utility
derived using a utility maximization approach. Thorough evaluations with and
without edge servers on simulation and real-world multi-robot systems
demonstrate that a lower task latency, a large throughput gain, and better
frame rate processing may be achieved compared to the conventional edge-based
systems.Comment: Accepted to the IEEE Intelligent Robotic Computing (IRC) Conference
202
Utility AI for Dynamic Task Offloading in the Multi-Edge Infrastructure
To circumvent persistent connectivity to the cloud infrastructure, the
current emphasis on computing at network edge devices in the multi-robot domain
is a promising enabler for delay-sensitive jobs, yet its adoption is rife with
challenges. This paper proposes a novel utility-aware dynamic task offloading
strategy based on a multi-edge-robot system that takes into account
computation, communication, and task execution load to minimize the overall
service time for delay-sensitive applications. Prior to task offloading,
continuous device, network, and task profiling are performed, and for each task
assigned, an edge with maximum utility is derived using a weighted utility
maximization technique, and a system reward assignment for task connectivity or
sensitivity is performed. A scheduler is in charge of task assignment, whereas
an executor is responsible for task offloading on edge devices. Experimental
comparisons of the proposed approach with conventional offloading methods
indicate better performance in terms of optimizing resource utilization and
minimizing task latency.Comment: Accepted to the IEEE Intelligent Robotic Computing (IRC) Conference
202
Improving Makespan in Dynamic Task Scheculing for Cloud Robotic Systems with Time Window Constraints
A scheduling method in a robotic network cloud system with minimal makespan
is beneficial as the system can complete all the tasks assigned to it in the
fastest way. Robotic network cloud systems can be translated into graphs where
nodes represent hardware with independent computing power and edges represent
data transmissions between nodes. Time-window constraints on tasks are a
natural way to order tasks. The makespan is the maximum amount of time between
when a node starts executing its first scheduled task and when all nodes have
completed their last scheduled task. Load balancing allocation and scheduling
ensures that the time between when the first node completes its scheduled tasks
and when all other nodes complete their scheduled tasks is as short as
possible. We propose a new load balancing algorithm for task allocation and
scheduling with minimal makespan. We theoretically prove the correctness of the
proposed algorithm and present simulations illustrating the obtained results.Comment: This work has been submitted to the IEEE for possible publication.
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A Comprehensive Survey of the Tactile Internet: State of the art and Research Directions
The Internet has made several giant leaps over the years, from a fixed to a
mobile Internet, then to the Internet of Things, and now to a Tactile Internet.
The Tactile Internet goes far beyond data, audio and video delivery over fixed
and mobile networks, and even beyond allowing communication and collaboration
among things. It is expected to enable haptic communication and allow skill set
delivery over networks. Some examples of potential applications are
tele-surgery, vehicle fleets, augmented reality and industrial process
automation. Several papers already cover many of the Tactile Internet-related
concepts and technologies, such as haptic codecs, applications, and supporting
technologies. However, none of them offers a comprehensive survey of the
Tactile Internet, including its architectures and algorithms. Furthermore, none
of them provides a systematic and critical review of the existing solutions. To
address these lacunae, we provide a comprehensive survey of the architectures
and algorithms proposed to date for the Tactile Internet. In addition, we
critically review them using a well-defined set of requirements and discuss
some of the lessons learned as well as the most promising research directions
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