Jiguo Dai

Papers

Total Citations

H-Index

About

Jiguo Dai is a leading researcher in autonomous robotics, with a primary focus on motion planning for autonomous underwater vehicles (AUVs). His most notable contribution is the development of the Liveness-Based Rapidly Exploring Random Tree (Li-RRT) algorithm, a novel approach that significantly enhances the safety and efficiency of path planning in complex, obstacle-rich underwater environments. By integrating the concept of "liveness"—a formal guarantee that a vehicle will not enter a deadlock state—Dai’s work bridges the gap between theoretical motion planning and practical, real-time navigation under resource constraints. His seminal 2017 paper on Li-RRT has garnered 15 citations, serving as a foundational reference for researchers tackling the challenge of robust AUV autonomy. Beyond this, Dai’s broader research addresses critical issues in robotic navigation, including obstacle avoidance and energy-efficient trajectory generation. His work is particularly impactful for applications in ocean exploration, environmental monitoring, and underwater search-and-rescue missions, where reliable motion planning is essential. Dai continues to push the boundaries of autonomous systems, inspiring new generations of engineers to develop safer, more intelligent robotic platforms.