Adaptive Energy Optimization for Edge-Enabled Autonomous Mobile Robots

In this work, we propose an advanced energy consumption model for an Autonomous Mobile Robots and validate the model on a real-world robot testbed. We show how our model can enable intelligent offloading of computationally heavy functions from the robot to an Edge server, over a wireless network, to minimize robot's energy consumption and maximize operating time on battery. Furthermore, we also present practical scenarios to show how our energy model can enable real-time adaptation of the Edge robotics system to constraints such as compute availability on the Edge server, available wireless network bandwidth, robot-specific camera frame rate requirements, robot navigation speeds and thereby improve robot energy efficiency. We show the benefits of our approach by offloading computationally heavy SLAM function from robot to the Edge server in simulation and through experiments on a real-world hardware testbed.