Computation offloading for ground robotic systems communicating over WiFi – an empirical exploration on performance and energy trade-offs

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.