Cloud robotics in smart cities is an emerging paradigm that enables autonomous robotic agents to communicate and collaborate with a cloud computing infrastructure.
It complements the Internet of Things (IoT) by creating an expanded network where robots offload data-intensive computation to the ubiquitous cloud to ensure quality of service (QoS).
However, offloading for robots is significantly complex due to their unique characteristics of mobility, skill-learning, data collection, and decision-making capabilities.
In this paper, a generic cloud robotics framework is proposed to realize smart city vision while taking into consideration its various complexities.
Specifically, we present an integrated framework for a crowd control system where cloud-enhanced robots are deployed to perform necessary tasks.
The task offloading is formulated as a constrained optimization problem capable of handling any task flow that can be characterized by a Direct Acyclic Graph (DAG).
We consider two scenarios of minimizing energy and time, respectively, and develop a genetic algorithm (GA)-based approach to identify the optimal task offloading decisions.
The performance comparison with two benchmarks shows that our GA scheme achieves desired energy and time performance.
We also show the adaptability of our algorithm by varying the values for bandwidth and movement.
The results suggest their impact on offloading.
Finally, we present a multi-task flow optimal path sequence problem that highlights how the robot can plan its task completion via movements that expend the minimum energy.
This integrates path planning with offloading for robotics.
To the best of our knowledge, this is the first attempt to evaluate cloud-based task offloading for a smart city crowd control system
