Hello! I am Yi Wang, a PhD student in robotics at the University of New Hampshire (UNH).

I am an extremely self-motivated researcher with a strong focus on developing state-of-the-art algorithms in robot motion planning. My work addresses open questions in motion planning, with a particular interest in bidirectional heuristic search.

My research centers on algorithmic robot motion planning for kinodynamic system, bidirectional heuristic search, and optimal controllers. I’m passionate about pushing the boundaries of autonomous systems by bridging theoretical insights with real-world applications.

I am currently collaborating with Professor Oren Salzman and Dr. Eyal Weiss at Technion-Israel Institute of Technology to optimize the interplay between sampling-based motion planning and bidirectional heuristic search—a novel framework that significantly improves solution-finding efficiency in high-dimensional continuous state spaces.

Recent Posts

Bidirectional Lazy Informed Trees (BLIT*), Proceedings of ICRA 2025, 19–23 May, Atlanta, USA.

Meet-in-the-middle with Early and Efficient Termination (MEET), Proceedings of IJCAI 2025, August 16-22, Montreal, Canada.

EMPC-Based Flight Control and Collision-Free Path Planning for A Quadrotor with Unbalanced Payload. Proceedings of IEEE/ASME Transactions on Mechatronics (TMECH) 2025. [pdf]

Honors and Awards

Special IJCAI 2025 DC Travel Award, awarded by the National Science Foundation (NSF) to selected U.S.-based Ph.D. students with accepted proceedings paper at IJCAI 2025.

Open soruce contributions

We have released C++ implementation of BLIT* for comparison-version via github.

Planning to publish BLIT* to Open Motion Planning Library OMPL soon!

Highlights of My Work

(1) Bidrectional Lazy Informed Trees (BLIT*):

Oral presentation at ICRA 2025, May 20, Atlanta, GA, USA.

Poster Presentation: BLIT_poster.pdf

(2) Meet-in-the-middle with Early and Efficient Termination (MEET)

Invited Oral Presentation to the DC of IJCAI25 at IJCAI 2025, Aug 18, Montreal, Canada.

(3) EMPC-Based Flight Control and Collision-Free Path Planning.

An experimental demonstration video is available here.