Eduardo Serrano
Papers
2
Total Citations
9
H-Index
2
About
Eduardo Serrano investigates the intersection of autonomous robotics and decision-making under uncertainty, with a focus on search strategies in high-variability environments. His work addresses a critical challenge: how robots can effectively locate targets when information about their own position and surroundings is inherently unreliable. In his most-cited paper (2021, 6 citations), Serrano demonstrates that while deterministic approaches succeed under low uncertainty, high-uncertainty domains require adaptive, environmentally modulated strategies—a finding with implications for search-and-rescue and planetary exploration. His earlier study (2017, 3 citations) on locomotive drift reveals how subtle movement errors can fundamentally alter the efficiency of scale-invariant search patterns, challenging assumptions in robotic path planning. Serrano’s contributions bridge theoretical models and practical robotics, offering frameworks that balance intrinsic robot limitations with external environmental factors. Though his citation counts are modest, his work is foundational for researchers tackling real-world autonomous search problems where precision is impossible and adaptability is essential.
Research Focus
Key Achievements
Top Papers
- 1
- 2Effects of Locomotive Drift in Scale-Invariant Robotic Search Strategies3 citations · 2017