K. Ashenayi

Papers

Total Citations

331

H-Index

About

K. Ashenayi is a prominent researcher in autonomous robotics, specializing in intelligent navigation, path planning, and evolutionary computation. His work sits at the intersection of artificial intelligence and mobile robotics, with a particular focus on developing sophisticated algorithms that enable robots to navigate complex environments without human intervention. Ashenayi's most influential contribution is his 2005 paper on genetic algorithm-based local path planning for mobile robots, which has garnered 186 citations and established a foundational approach to obstacle avoidance that balances validity with optimality. Building on this, his 2007 work on genetic algorithms for autonomous robot navigation — cited 115 times — extended these principles into practical instrumentation and measurement contexts where manual data collection proves impractical or dangerous. His 2006 study on evolving diverse collections of path planning problems demonstrated additional creativity, introducing evolutionary computation techniques to systematically generate and taxonomize robot navigation challenges. Beyond algorithmic research, Ashenayi has contributed to robot vision systems integrating fuzzy logic and neural networks, demonstrated through a competition vehicle that successfully navigated obstacle courses at Disney World in 1996. Collectively, his work has meaningfully advanced autonomous robotics, offering tools and frameworks that continue to influence both academic research and real-world applications.