Resource Manager for Scalable Performance in ROS Distributed Environments

Abstract

This paper presents a resource manager to achieve scalable performance in Robot Operating System (ROS) for distributed environments. In robotics, using ROS in distributed environments via multiple host machines is trending for large-scale data processing, for example, cloud/edge computing and the data communication of point clouds and images in dynamic map composition. However, ROS is unable to manage the resources (e.g., the CPUs, memory, and disks) on each host machine. Therefore, it is difficult to use distributed environmental resources efficiently and achieve scalable performance. This paper proposes a resource management mechanism for ROS distributed environments using a master-slave model to execute ROS processes efficiently and smoothly. We manage the resource usage of each host machine and construct a mechanism to adaptively distribute the load to be balanced. Evaluations show that scalable performance can be achieved in ROS distributed environments comprising ten host machines using a real application (SLAM: simultaneous localization and mapping) processing large-scale point cloud data.