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Home Page for Hongsen Qin | Parasol Laboratory

Picture Hongsen Qin
High School Student
Algorithms & Applications Group

Parasol Laboratory url: http://parasol.tamu.edu/~hqin/
Department of Computer Science and Engineering email:
Texas A&M University office: 407 HRBB
College Station, TX 77843-3112 tel:
USA fax: (979) 458-0718


Hello! My name is Hongsen Qin and I am a Senior at College Station High School. I am interested in studying computer science and develop applications related to motion-planning and robotics. I have a passion for science and technology and my favorite subject in school is physics. In addition to my school work, I participate in science competitions such as the Chemistry Olympiad and is a state recognized science contest champion. When I'm not playing with c++, I enjoy basketball games with friends.

In the summer of 2015 I participated in a research internship in the Algorithms and Applications Group at Parasol Laboratory. My faculty advisor was Dr. Nancy Amato.My postdoctoral advisor was Dr. Jory Denny, who now works at the University of Richmond. I worked on a collaborative planning project focusing on the abstraction and quantification of various collaborative planning technique, the results of which can be applied in developing collaborative systems in robotics motion control. I implemented user-input models and the analysis techniques for this project. Details about the project can be found below.

On the Theory of User-Guided Planning


Sampling-based techniques are often employed to solve various complex motion planning problems, the problem of computing a valid path under various robot and/or obstacle constraints. As these methods are random in nature, the probability of their success is directly related to the expansiveness, or openness, of the underlying planning space. However, little is known theoretically in qualifying the conditions under which and quantifying how much user (human)-guided approaches improve this success rate. In this paper, we classify and create simplistic models of common user-guided approaches, extend the concept of expansiveness to analyze these models to understand both when and how much user-guidance aids sampling-based planners. Further, we experimentally demonstrate our new theory in a few isolated problems.

My IROS publication for this project can be found here, and on Semantic Scholar

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