Grant Bruer

University of Tennessee at Knoxville, Georgia Institute of Technology

Papers

Total Citations

H-Index

About

Grant Bruer is a pioneering researcher at the intersection of neuromorphic computing and autonomous robotics, whose work centers on developing brain-inspired control systems for intelligent machines. His major contributions include the creation of the Dynamic Adaptive Neural Network Array (DANNA) framework, a novel neuromorphic architecture that enables real-time, energy-efficient robotic navigation. Bruer’s most influential paper, "NeoN: Neuromorphic control for autonomous robotic navigation" (2017, 42 citations), introduces a groundbreaking system that allows a roaming robot to perform obstacle avoidance using a spiking neural network—a key step toward low-power, biologically plausible robotics. Building on this, his work "GRANT: Ground-Roaming Autonomous Neuromorphic Targeter" (2020, 5 citations) demonstrates the first fully integrated neuromorphic robot capable of obstacle avoidance, grid coverage, and targeting, all controlled by the second-generation DANNA2 digital spiking processor. As a core member of the TENNLab (Laboratory of Tennesseans Exploring Neural Networks), Bruer has helped bridge the gap between theoretical neuromorphic models and practical hardware implementations, paving the way for next-generation autonomous systems that operate with minimal energy consumption. His research continues to inspire advances in edge computing and intelligent robotics.