Daniel Acosta

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About

Daniel Acosta is a robotics researcher whose work centers on autonomous navigation and control systems for mobile robots, with a particular focus on assistive technologies. His key research areas include localization, odometric modeling, and advanced control strategies for wheeled platforms. Acosta’s major contribution lies in integrating machine learning and fractional-order control to enhance the precision and responsiveness of autonomous vehicles. In his highly cited 2023 paper, “Improving Odometric Model Performance Based on LSTM Networks,” he demonstrated how a Long Short-Term Memory neural network can significantly refine odometric localization for an autonomous wheelchair, fusing data from LIDARs, IMUs, and wheel encoders. This work, with 9 citations, directly improves the reliability of real-world navigation systems. His complementary study, “Improving Mobile Robot Maneuver Performance Using Fractional-Order Controller,” tackles the limitations of traditional PID controllers in handling ramp velocity references, proposing a more robust alternative for low-level speed control. With a growing citation footprint, Acosta’s research is paving the way for safer, more adaptive autonomous mobility solutions, particularly in healthcare and service robotics.