Fernando Ramos
Papers
6
Total Citations
52
H-Index
4
About
Fernando Ramos is a pioneer in applying evolutionary computation and cooperative intelligence to robotics and manufacturing systems. His research focuses on multi-robot path planning, cooperative algorithms, and self-learning environments, with a particular emphasis on collision-free coordination in shared workspaces. Ramos introduced the concept of **Cooperative Genetic Algorithms (CGA)**, a novel approach where each robot is assigned its own population, enabling simultaneous path planning without collisions—a breakthrough detailed in his highly cited 2002 work (14 citations). He also developed **Cooperative Simulated Annealing** for multi-robot systems (2000, 15 citations), demonstrating how metaheuristics can solve complex coordination tasks. Beyond path planning, Ramos explored behavioral tracking and pattern recognition in competitive multi-agent domains, such as robotic soccer (2010, 6 citations; 2007, 4 citations), and advanced problem-based learning (PBL) environments for robotics education (2003, 10 citations). His work on adapting messy genetic algorithms for redundant manipulators (2002, 3 citations) further showcases his versatility. With over 50 citations across his most impactful papers, Ramos’s contributions remain foundational for researchers tackling cooperative robotics and evolutionary optimization.
Research Focus
Key Achievements
Top Papers
- 1Cooperative Simulated Annealing for Path Planning in Multi-robot Systems15 citations · 2000
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- 4Tracking behaviours of cooperative robots within multi-agent domains6 citations · 2010
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