Abstract Context Robotic systems are known to perform computation-intensive tasks with limited computational resources and battery life. Such systems might benefit from offloading heavy workloads to the Cloud; however, in some cases, this implies high network traffic that degrades performance and energy consumption. Goal In this study, we aim at evaluating the impact of different computation offloading strategies on performance and energy consumption in the context of autonomous robots. Method We conduct two controlled experiments involving a robotic mission based on the Turtlebot3 robot and ROS 1. The mission consists of three tasks that are recurrent in robotics and good candidates for computation offloading in research, namely, SLAM mapping, navigation stack, and object recognition. Each of the tasks is either executed on board or offloaded in a full-factorial experiment design. The obtained measures are then statistically analyzed. Results The results show that offloading the object recognition task causes a more significant decrease in resource utilization and energy consumption than both SLAM mapping and navigation. However, object recognition affects the volume of network traffic significantly to the extent that it can easily cause network congestion. Conclusions In the context of our experiments (i.e.,those involving small-scale ground ROS-based mobile robots operating under WiFi networks), offloading object recognition is beneficial in terms of performance and energy consumption. Nevertheless, large network bandwidth needs to be available for object recognition offloading. While the image resolution and frame rate have a significant impact on not only the network traffic but also energy consumption and performance, these parameters need to be carefully set so that the results of this task can be always received in time, which is particularly crucial in real-time systems.

The Robot Operating System (ROS) has become the de-facto standard framework for robotics software, and a great part of commercial robots is expected to have at least one ROS package on board in the coming years. For good quality, robotics software should rely on strong software engineering principles. In this paper, we perform a systematic mapping study on several works in software engineering on ROS, published at the top software engineering and robotics venues. Our goal is to analyze and evaluate such state-of-the-art regarding its relevance to the robotics software industry. The potentially-relevant studies are subject to a rigorously defined selection process. This results in a set of 63 primary studies on software engineering research on ROS. Those primary studies are then qualitatively analyzed according to a rigorously-defined classification framework. The results are of interest to both researchers and practitioners: (i) we provide an up-to-date overview of the state of the art on software engineering research on ROS and its potential for industrial adoption, (ii) a broad discussion of the research area as a whole, and (iii) point out routes of action for a better alignment between research and industry.