Papers
3
Total Citations
20
H-Index
2
About
Wenhao Xu is a robotics researcher focused on autonomous navigation and control for extreme environments, particularly deep-sea mining. His work addresses critical challenges in operating vehicles in unstructured, hazardous underwater settings where traditional sensing and control methods fall short. Xu’s most impactful contribution is his 2022 paper on an error-state Kalman filter-based localization algorithm with velocity estimation for deep-sea mining vehicles, which has garnered 16 citations. This work provides a robust solution for accurate positioning in GPS-denied underwater environments, a fundamental requirement for autonomous mining operations. More recently, Xu has advanced the field with his 2025 study on optimized deep reinforcement learning for dual-task control—simultaneously handling path following and obstacle avoidance for deep-sea mining robots. This work proposes a novel training framework that overcomes the limitations of conventional strategies in complex, unstructured terrains. Earlier in his career, Xu explored low-cost exploration strategies with his 2021 paper on an automotive unknown-environment explorer robot based on Braitenberg Vehicle Four, demonstrating how simple sensors can achieve effective environmental mapping. Through these contributions, Xu is establishing himself as a key innovator in deep-sea robotics, pushing the boundaries of autonomous operation in one of Earth’s most challenging frontiers.
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