Dingjie Xu
Papers
3
Total Citations
15
H-Index
3
About
Dingjie Xu is a robotics researcher whose work focuses on underground environmental perception and manipulator motion planning. His key contributions include developing a ground-penetrating radar-based perception system for robotic platforms, enabling robots to sense and navigate underground spaces—a relatively underexplored area in environmental perception technology. This work has garnered 6 citations and addresses a critical gap in mobile robotics. Xu has also advanced inverse kinematics for robotic manipulators in complex, overhead multi-line environments, proposing an obstacle-avoidance method that efficiently finds feasible solutions where traditional approaches struggle. His fast collision detection technique, based on point clouds and stretched primitives, further enhances manipulator obstacle-avoidance motion planning by maintaining both accuracy and computational efficiency, even with non-convex obstacles and complex link geometries. With a total of 15 citations across his most-cited papers, Xu’s research is steadily gaining recognition for its practical relevance to autonomous systems operating in cluttered or subterranean settings. His work represents an important step toward more capable and safer robotic manipulation in real-world industrial and exploration scenarios.
Research Focus
Key Achievements
Top Papers
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