Brandon Zahn

University of Newcastle Australia, Tokyo Metropolitan University

Papers

Total Citations

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About

Brandon Zahn is a robotics researcher whose work focuses on the optimization of autonomous systems through bio-inspired algorithms and simulation-based control. His most influential contributions center on enhancing robot movement and control strategies, particularly for line-tracing and trajectory-following tasks. Zahn’s 2016 paper, “Optimization of a Proportional-Summation-Difference Controller for a Line-Tracing Robot Using Bacterial Memetic Algorithm,” introduced a novel approach to fine-tuning control parameters by leveraging the bacterial memetic algorithm, demonstrating how evolutionary computation can improve real-time robot performance. In 2019, he extended this line of inquiry with “Optimization of Robot Movements Using Genetic Algorithms and Simulation,” which applied genetic algorithms to refine motion planning in simulated environments, bridging the gap between theoretical optimization and practical robotics. Though his published work has garnered modest citation counts—each of his two most-cited papers receiving four citations—these contributions represent foundational steps in applying computational intelligence to low-cost, accessible robotic platforms. Zahn’s research is particularly valuable for students and hobbyists exploring how optimization techniques can be implemented in resource-constrained systems, offering clear, reproducible methodologies that advance the field of autonomous robotics.