A Distributed Control Architecture for Resource-constrained Autonomous Systems

Abstract

The use of autonomous systems is burgeoning in the world today for many applications in many fields from scientific, industrial, to military. At the same time, advances in semiconductor technology have enabled ever smaller, complex and dedicated microprocessors. This work details a control system architecture that takes advantage of these advances for use in resource-constrained autonomous systems. The architecture consists of a real time hardware controller, a guidance and navigation computer, and an edge TPU for machine learning inferences. While the latter two processors are commercially available, a dedicated, modular real time controller is not. Therefore we present an open source design for a real time controller that is intended to be adapted to many types of autonomous systems. We present three different vehicle platforms that implement this control system including a ground vehicle, a surface vessel, and a quadcopter. Finally, we present results using this control architecture on a few key topics of interest in autonomous systems. The first is a novel spatial estimation algorithm called partitioned ordinary kriging that is designed for resource-constrained systems and can be used for path finding during mapping missions. The second result pertains to a sensor calibration utilizing sensor data collected from the real time controller and performed on the navigation computer. Finally, we demonstrate using the edge TPU (tensor processing unit) for object detection using an onboard camera and an object detection algorithm using machine learning. © 2022 IEEE.