A Self-Aware and Scalable Solution for Efficient Mobile-Cloud Hybrid Robotics - Akbar, Aamir; Lewis, Peter R.; Wanner, Elizabeth (Frontiers in Robotics and AI, 2020)
Many benefits of cloud computing are now well-established. Backed by the virtually unbounded resources of the cloud, battery-powered mobile robotics can also benefit from cloud computing, meeting the demands of even the most computationally and resource-intensive tasks. However, many existing mobile-cloud hybrid robotic tasks are inefficient in terms of optimising trade-offs between simultaneously conflicting objectives, such as minimising both battery power consumption and network usage. To tackle this problem we propose a novel approach that can be used not only to instrument a mobile-cloud hybrid robotic task but also to search for its efficient configurations representing compromise solution between the objectives. We introduce a general purpose MC hybrid framework to measure, at runtime, how well the tasks meet these two objectives. The framework employs these efficient configurations to make decisions at runtime, which are based on: 1) changing of the environment (i.e. WiFi signal level variation), and 2) itself in a changing environment (i.e. actual observed packet loss in the network). Also, we introduce a Two-Step search-based multi-objective optimisation (MOO) algorithm to search for efficient configurations. Our experimental evaluation considers a Python-based foraging task performed by a battery-powered and Raspberry Pi controlled Thymio robot. Analysis of our results shows that: (i) using self-adaptive and self-aware decisions, a mobile-cloud hybrid foraging task performed by a battery-powered robot can achieve better optimisation in a changing environment than using static offloading or running the task only on the robot. However, a self-adaptive decision would fall behind when the change in the environment happens within the system. In such a case, a self-aware system can perform well, in terms of minimising the two objectives. (ii) The Two-Step algorithm can search better quality configurations for MC hybrid robotic tasks of having size from small to medium scale, in terms of total number of their offloadable modules.