Yuanqi Mao
Papers
1
Total Citations
13
H-Index
1
About
Yuanqi Mao is a leading researcher in stochastic motion planning and real-time autonomous navigation, with a focus on enabling safe decision-making under uncertainty. His most cited work, "Stochastic Motion Planning Using Successive Convexification and Probabilistic Occupancy Functions" (2018, 13 citations), introduces a groundbreaking framework that combines forward stochastic reachability analysis with non-convex optimization in a receding horizon setting. This approach allows autonomous systems to generate dynamically feasible trajectories in real time, even within unpredictable, dynamic environments—a critical capability for applications in robotics, self-driving vehicles, and aerial drones. By leveraging probabilistic occupancy functions to model environmental uncertainty and successive convexification to solve complex optimization problems efficiently, Mao’s method bridges the gap between theoretical safety guarantees and practical computational demands. His contributions have been recognized for advancing the field of risk-aware motion planning, offering a scalable solution that balances performance with rigorous safety constraints. Mao’s work continues to inspire researchers tackling the challenges of autonomous systems operating in the real world, where uncertainty is inevitable and split-second decisions are paramount.
Research Focus
Key Achievements
Top Papers
- 1