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About

J. Batlle is a pioneering researcher in robotics and computer vision, with key contributions spanning autonomous underwater vehicles (AUVs), control architectures, and 3D perception. Their most-cited work, "Designing a Fuzzy-like PD controller for an underwater robot" (67 citations), demonstrates innovative control strategies for challenging marine environments. Batlle’s influential review "Recent trends in control architectures for autonomous underwater vehicles" (63 citations) systematically analyzed 22 architectures, establishing foundational knowledge in the field. They also advanced stereovision through "Recent progress in structured light to solve the correspondence problem" (40 citations), summarizing critical coded light techniques for 3D depth perception. More recently, Batlle explored rehabilitation robotics with "IoT Architecture for Smart Control of an Exoskeleton Robot" (27 citations), integrating natural user interfaces and gesture control. Their work on modular neural networks for grasping tasks (22 citations) and low-cost underwater vehicles like GARBI (16 citations) further showcases versatility. Batlle’s research has accumulated over 270 citations, reflecting sustained impact across robotics, control systems, and computer vision. Their contributions to behavior-based control and reinforcement learning for autonomous robots highlight a career dedicated to advancing intelligent, adaptive robotic systems.