Enabling Network-Aware Cloud Networked Robots with Robot Operating System : A machine learning-based approach

Sammanfattning: During the recent years, a new area called Cloud Networked Robotics (CNR) has evolved from conventional robotics, thanks to the increasing availability of cheap robot systems and steady improvements in the area of cloud computing. Cloud networked robots refers to robots with the ability to offload computation heavy modules to a cloud, in order to make use of storage, scalable computation power, and other functionalities enabled by a cloud such as shared knowledge between robots on a global level. However, these cloud robots face a problem with reachability and QoS of crucial modules that are offloaded to the cloud, when operating in unstable network environments. Under such conditions, the robots might lose the connection to the cloud at any moment; in worst case, leaving the robots “brain-dead”. This thesis project proposes a machine learning-based network aware framework for a cloud robot, that can choose the most efficient module placement based on location, task, and the network condition. The proposed solution was implemented upon a cloud robot prototype based on the TurtleBot 2 robot development kit, running Robot Operating System (ROS). A continuous experiment was conducted where the cloud robot was ordered to execute a simple task in the laboratory corridor under various network conditions. The proposed solution was evaluated by comparing the results from the continuous experiment with measurements taken from the same robot, with all modules placed locally, doing the same task. The results show that the proposed framework can potentially decrease the battery consumption by 10% while improving the efficiency of the task by 2.4 seconds (2.8%). However, there is an inherent bottleneck in the proposed solution where each new robot would need 2 months to accumulate enough data for the training set, in order to show good performance. The proposed solution can potentially benefit the area of CNR if connected and integrated with a shared-knowledge platform which can enable new robots to skip the training phase, by downloading the existing knowledge from the cloud.