Javier Sedano

Technological Institute of Castilla y León, Universidad de Burgos

Papers

Total Citations

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About

Javier Sedano is a leading researcher in artificial intelligence, robotics, and industrial process optimization, with a focus on multi-robot path planning and metaheuristic algorithms. His most influential work, "An efficient multi-robot path planning solution using A* and coevolutionary algorithms" (2022, 43 citations), addresses the real-world challenge of coordinating multiple robots in environments like warehouses, combining A* search with coevolutionary techniques to generate collision-free, efficient paths. This contribution is pivotal as multi-robot systems transition from theory to practical deployment. Earlier, Sedano demonstrated the power of AI in manufacturing with "The application of a two-step AI model to an automated pneumatic drilling process" (2009, 18 citations), where he integrated unsupervised connectionist models and system identification to enhance industrial drilling precision—showcasing his ability to bridge computational intelligence with tangible engineering problems. His recent work, "Slime Mould Metaheuristic for optimization and robot path planning" (2025, 2 citations), explores bio-inspired optimization, drawing from slime mould behavior to solve complex path planning and function optimization challenges. Across his career, Sedano’s research has consistently advanced autonomous systems, earning recognition for practical, scalable solutions that impact both academia and industry.