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About

Hoang Anh Pham is a robotics researcher whose work bridges the gap between autonomous underwater vehicles and multi-agent coordination, with a particular focus on low-cost, accessible platforms. His most significant contribution is the development of **Distributed Adaptive Neural Network Control (DANNC)** for formation tracking of underwater drones in hazardous environments—a paper that has garnered 10 citations and addresses critical challenges in underwater vehicle dynamics. Pham has also made notable strides in **relative localization estimation** for low-cost underwater drones, using only visual feedback from onboard cameras and IMU data to enable coordinated formation control without expensive sensors. His research extends to **multi-agent decision-making**, where he applies probabilistic approaches like Dec-POMDP to soccer robotics, and he has recently explored **perception challenges in mixed robot-human swarms**, investigating how cognitive uncertainty is reduced through shared perception. Pham’s work on the RoboCup Vision shared dataset and his comprehensive review of sensing strategies for multi-robot systems further underscore his commitment to advancing swarm intelligence. With a growing citation record and a focus on practical, cost-effective solutions, Pham is shaping the future of collaborative robotics in both aquatic and terrestrial domains.