Ukamaka Hope Agwogie
Papers
2
Total Citations
17
H-Index
2
About
Ukamaka Hope Agwogie is a researcher at the forefront of computational intelligence and mobile robotics, specializing in bio-inspired optimization algorithms for autonomous navigation. Her work focuses on solving the critical challenge of mobile robot path planning—enabling robots to navigate complex environments efficiently and optimally. Agwogie’s major contributions include comprehensive reviews and comparative analyses of swarm intelligence techniques, such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and the emerging Fruit Fly Optimization Algorithm (FOA). Her seminal 2018 review, "Swarm Intelligence Optimization Techniques in Mobile Path Planning - A Review," has garnered 11 citations, establishing a foundational resource for researchers in the field. In her 2020 study, she empirically demonstrated the performance of these algorithms in obstacle-free static environments, providing critical insights into their relative strengths. With a combined citation count of 17 across her most-cited works, Agwogie’s research is shaping the next generation of autonomous systems. Her work is particularly notable for introducing FOA—a novel, nature-inspired algorithm—to the path planning domain, offering a fresh perspective on optimization in robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2