Resource Constraint adaptive computing: Algorithm and optimization for ARL Autonomy Stack

This internship opportunity from the Army Research Laboratory at Aberdeen Proving Ground is designed for graduate students who are U.S. citizens or green card holders. The ARL team focuses on optimizing computationally expensive perception algorithms within the Autonomy stack for autonomous vehicles. The research addresses the challenge of processing large amounts of sensor data (RGB camera and Lidar) through multiple perception algorithms while operating under strict Size, Weight, and Power (SWaP) constraints typical of tactical unmanned ground vehicles (UGVs). The intern will work closely with ARL researchers to optimize and integrate containerized machine learning algorithms into UGVs, ensuring real-time operation with limited computing resources. Activities include using Python and C++ for algorithm optimization, implementing containerization technologies like Docker, deploying algorithms in ROS environments, and documenting results for technical reports or conference papers. This position is part of the Army Research Laboratory Research Associateship Program (ARL-RAP), which aims to increase involvement of highly trained scientists and engineers in Army-relevant research areas.