Matthew Oluwole Arowolo
Papers
2
Total Citations
9
H-Index
2
About
Matthew Oluwole Arowolo is a researcher specializing in robotics, control systems, and intelligent automation, with a focus on integrating evolutionary algorithms into real-world engineering applications. His work bridges the gap between theoretical optimization and practical deployment, particularly in autonomous systems and industrial automation. Arowolo’s most cited paper, "PROTOTYPE LINE FOLLOWING AUTOMATIC GUIDED VEHICLE (AGV) FOR UNIT LOAD DISPATCH IN AN OFFICE ENVIRONMENT" (2019, 5 citations), introduces a cost-effective AGV solution for automating mundane clerical tasks, demonstrating how robotics can enhance productivity in low-tech environments. His second key contribution, "The effect of an evolutionary algorithm's rapid convergence on improving DC motor response using a PID controller" (2022, 4 citations), tackles a critical challenge in mobile robotics—DC motor speed control—by proposing a particle swarm optimization (PSO)-tuned PID controller that outperforms conventional methods. This work highlights his ability to address real-world path-tracking issues through algorithmic innovation. Arowolo’s research is notable for its practical impact, offering scalable solutions for office automation and robotic mobility. With a growing citation record, his contributions are gaining recognition among engineers and researchers seeking to optimize autonomous systems for efficiency and reliability.
Research Focus
Key Achievements
Top Papers
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