Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey
Mahbuba Afrin, Jiong Jin, Akhlaqur Rahman, Ashfaqur Rahman, Jiafu Wan, and Ekram Hossain

TL;DR
This survey reviews the state-of-the-art in resource allocation and service provisioning in multi-agent cloud robotics, highlighting challenges, taxonomies, and future research directions for optimizing robotic applications using cloud and edge computing.
Contribution
It provides the first comprehensive taxonomy of resource allocation in multi-agent cloud robotics and discusses key techniques like resource pooling, offloading, and scheduling.
Findings
Identified key challenges in resource allocation for robotic tasks.
Presented a taxonomy for resource management strategies.
Highlighted future research directions in the field.
Abstract
Robotic applications nowadays are widely adopted to enhance operational automation and performance of real-world Cyber-Physical Systems (CPSs) including Industry 4.0, agriculture, healthcare, and disaster management. These applications are composed of latency-sensitive, data-heavy, and compute-intensive tasks. The robots, however, are constrained in the computational power and storage capacity. The concept of multi-agent cloud robotics enables robot-to-robot cooperation and creates a complementary environment for the robots in executing large-scale applications with the capability to utilize the edge and cloud resources. However, in such a collaborative environment, the optimal resource allocation for robotic tasks is challenging to achieve. Heterogeneous energy consumption rates and application of execution costs associated with the robots and computing instances make it even more…
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Taxonomy
TopicsRobotics and Automated Systems · IoT and Edge/Fog Computing · Modular Robots and Swarm Intelligence
