Weizhuang Ma

Shandong University of Science and Technology

Papers

Total Citations

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About

Weizhuang Ma is a rising researcher in the field of bio-inspired robotics and intelligent control, with a primary focus on autonomous navigation for underwater vehicles. His most notable contribution is the development of an improved deep Q-network (DQN) algorithm for path planning in bionic robotic fish operating under the influence of ocean currents. This work, published in 2025, addresses a critical challenge in marine robotics: enabling energy-efficient, adaptive navigation in dynamic and unpredictable aquatic environments. By integrating reinforcement learning with hydrodynamic constraints, Ma’s approach allows robotic fish to learn optimal trajectories in real time, significantly enhancing their autonomy and robustness. Although early in his career, his research has already garnered attention, with his flagship paper accumulating 3 citations—a promising start for a novel methodology. Ma’s work bridges the gap between artificial intelligence and biomechanics, offering practical solutions for environmental monitoring, underwater exploration, and search-and-rescue missions. His innovative use of deep reinforcement learning in bionic systems positions him as a forward-thinking contributor to the next generation of autonomous underwater vehicles.