Optimal Algorithm Allocation for Robotic Network Cloud Systems
This addresses the challenge of efficient resource management in cloud robotics for robotic networks, though it appears incremental as it builds on prior work focusing on cost minimization.
The paper tackles the problem of optimally allocating algorithms in robotic network cloud systems to achieve optimal performance regardless of which robot initiates a request, resulting in improvements in memory requirements and task completion time compared to a state-of-the-art method.
A robotic network is a system with multiple robots connected by a communication network. Certain tasks that cannot be accomplished with available robotic resources are candidates for the use of cloud robotics, which overcomes the limitations of the robot network by adding to the network, either local or remote servers or cloud infrastructure, to aid in computational demanding tasks or storage. Previous studies have mainly focused on minimizing the cost of the robots in retrieving resources by knowing the resource allocation in advance. We develop a method for a robotic network cloud system that includes robots, fog and cloud nodes, to determine where each algorithm should be allocated so that the system achieves optimal performance, regardless of which robot initiates the request. We can find the minimum required memory for the robots and the optimal way to allocate the algorithms with the shortest time to complete each task. We experimentally compare our method with a state-of-the-art method, using real-world data, showing the improvements that can be obtained.