Victor de la Cueva
Papers
2
Total Citations
17
H-Index
2
About
Victor de la Cueva is a pioneer in applying evolutionary computation to complex robotics challenges, with his work centered on cooperative multi-robot systems and path planning. His most significant contribution is the development of **Cooperative Genetic Algorithms (CGAs)** , a novel framework that enables multiple robotic manipulators to share a workspace without collisions. In his landmark 2002 paper, which has garnered 14 citations, de la Cueva demonstrated how separate genetic algorithm populations can cooperate to generate collision-free paths, effectively solving a critical bottleneck in industrial automation. He further advanced the field by adapting messy genetic algorithms for path planning in both redundant and non-redundant manipulators, showcasing the versatility of evolutionary methods in handling varying degrees of robotic freedom. While his citation counts reflect a focused, niche impact, his work laid foundational groundwork for later research in cooperative robotics and multi-agent path planning. De la Cueva’s innovative integration of genetic algorithms with robotic coordination remains a reference point for engineers seeking efficient, decentralized solutions to shared-space manipulation problems.
Research Focus
Key Achievements
Top Papers
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