  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 complex. In addition, the data transmission delay between local
robots, edge nodes, and cloud data centres adversely affects the real-time
interactions and impedes service performance guarantee. Taking all these issues
into account, this paper comprehensively surveys the state-of-the-art on
resource allocation and service provisioning in multi-agent cloud robotics. The
paper presents the application domains of multi-agent cloud robotics through
explicit comparison with the contemporary computing paradigms and identifies
the specific research challenges. A complete taxonomy on resource allocation is
presented for the first time, together with the discussion of resource pooling,
computation offloading, and task scheduling for efficient service provisioning.
Furthermore, we highlight the research gaps from the learned lessons, and
present future directions deemed beneficial to further advance this emerging
field.
