Papers
1
Total Citations
9
H-Index
1
About
Ziqiang Xia has carved a distinctive niche in the field of autonomous robotics, with a primary focus on the motion planning and control of nonholonomic mobile robots. His most-cited work, a 2020 paper on trajectory tracking and obstacle avoidance, introduces a sophisticated model predictive control (MPC) framework that elegantly solves the dual challenge of precise path following while safely navigating static obstacles. By formulating a quadratic cost function to compute optimal control inputs, Xia’s approach enables robots to maintain accurate trajectories without sacrificing real-time obstacle avoidance—a critical capability for applications in warehouse logistics and autonomous navigation. With 9 citations, this paper has established him as a rising contributor to MPC-based robotics. Xia’s research bridges the gap between theoretical control systems and practical robotic deployment, offering computationally efficient solutions that enhance robot autonomy in cluttered environments. His work continues to influence engineers and researchers seeking robust, real-time control strategies for mobile platforms operating in dynamic, obstacle-rich settings.
Research Focus
Key Achievements
Top Papers
- 1