Optimal task offloading for cloud networked robotics: a genetic algorithm approach

The thesis is on the topic of developing task offloading methods for Cloud Networked Robotics, where genetic algorithm based approaches are designed to optimally allocate tasks between robots and cloud virtual machines by utilizing the "on-demand" mobility and network selection features of the robot in the context of smart city and smart manufacturing applications, that results in lesser energy and completion time as well as increased efficiency of the system.