Ma Boweng
Papers
1
Total Citations
7
H-Index
1
About
Ma Boweng is a rising researcher in mobile robotics, whose work focuses on overcoming fundamental limitations in autonomous navigation and path planning. His most cited paper, "Integration of improved APF and RRT algorithms for enhanced path planning in mobile robotics" (2024, 7 citations), addresses critical shortcomings in traditional approaches. Specifically, he identifies that the Artificial Potential Field (APF) method alone often leads to local optimal solutions or failure to reach target points, while the Rapidly-exploring Random Tree (RRT) method suffers from inefficiency. By integrating improved versions of both algorithms, Ma proposes a hybrid framework that leverages APF’s goal-directed guidance and RRT’s probabilistic exploration, achieving more robust and efficient path generation. This contribution is particularly valuable for real-world applications where robots must navigate complex, dynamic environments without getting stuck. Though early in his career, Ma’s work demonstrates a keen ability to synthesize and enhance established techniques, offering practical solutions to persistent problems in mobile robotics. His research holds promise for advancing autonomous systems in fields such as warehouse logistics, search-and-rescue, and service robotics.
Research Focus
Key Achievements
Top Papers
- 1