Integration of Heterogeneous Computational Platform-Based, Ai-Capable Planetary Rover Using ROS 2

Space exploration has experienced a surge in interest and accessibility, with an increasing number of spacecraft launches. However, the scaling of space technology faces challenges as it heavily relies on human supervision and intervention. To overcome these limitations and enable greater autonomy, recent advancements in software and hardware, particularly in commercial off-the-shelf (COTS) components, have provided new opportunities. This paper introduces a prototype of a tightly-coupled hardware-software system that leverages a standard COTS computational platform and deep learning coprocessors to enable the efficient execution of deep learning workloads for space rovers. Integrated within the Robot Operating System 2 (ROS 2) framework, the system incorporates onboard sensors and offers rapid prototyping capabilities. By harnessing the benefits of COTS components and advanced software frameworks, this system represents a step towards achieving increased autonomy in space rovers, while also reducing development time. The presented system showcases the potential for future advancements in autonomous space exploration. The project documentation is publicly available: https://github.com/PUTvision/ros2_fpga_inference_node