Papers
2
Total Citations
4
H-Index
2
About
Long Ma is a robotics researcher whose work centers on motion planning and control for robotic manipulators, with a particular focus on developing intelligent algorithms for complex, real-world environments. His research addresses one of the fundamental challenges in robotics: enabling manipulators to navigate efficiently and safely through three-dimensional workspaces, including scenarios requiring multi-point traversal. Ma's contributions span two prominent algorithmic approaches. In his 2023 work, he introduced an improved ant colony algorithm featuring nonuniform initial pheromone concentration to enhance path planning efficiency for manipulators operating in complex environments—a biologically inspired method refined to overcome traditional limitations such as slow convergence and disorder in early iterations. His earlier 2019 study tackled multi-target traversal planning using an improved Rapidly-exploring Random Tree (RRT) algorithm, applied to a 6-DOF manipulator, demonstrating practical implementation of planning strategies for industrial and service robotics. Both papers have garnered 2 citations each, reflecting an emerging body of work still gaining recognition within the robotics community. Ma's research holds meaningful implications for manufacturing automation, human-robot collaboration, and intelligent robotic systems where reliable, adaptable path planning is critical to safe and efficient operation.
Research Focus
Key Achievements
Top Papers
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