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Home Page for Jyh-Ming Lien | Parasol Laboratory

Picture Jyh-Ming Lien
Ph.D. Student
Algorithms & Applications Group

Parasol Laboratory url: http://parasol.tamu.edu/~neilien/
Department of Computer Science and Engineering email:
Texas A&M University office: 407E HRBB
College Station, TX 77843-3112 tel: (979) 847-8835
USA fax: (979) 458-0718

My webpage at George Mason. (Jan 2007)

I am now a postdoc in the CITRIS Tele-Immersion lab at UC Berkeley, and, in January 2007, I will join the Department of Computer Science at the George Mason University. This page is no longer being maintained here. Here is my new webpage. (July 2006)

Lien, Jyh-Ming is a Ph.D. student in the Department of Computer Science at Texas A&M University. He currently works in the Algorithms & Applications Group in Parasol Lab with Nancy Amato. Lien's research is in the areas of computational geometry, computer graphics, and robotics.

[ CV ( pdf, txt ) ] [ Statement ( pdf, ps ) ]

Jyh-Ming Lien [ Home ] [ Research ] [ Publications ] [ Projects ] [ Conferences ] [ Seminars ] [ Personal ]


Approximate Convex Decomposition (ACD) and its Applications [ publications ]

Approximate Convex Decomposition (ACD)
We propose a partitioning strategy that decomposes a given model into "approximately convex" pieces. For many applications, the approximately convex components of this decomposition provide similar benefits as convex components, while the resulting decomposition is both significantly smaller and can be computed more efficiently. Indeed, for many models, an approximate convex decomposition can more accurately represent the important structural features of the model by providing a mechanism for ignoring insignificant features.

Skeleton Extraction
We present a novel skeleton extraction method using the approximate convex decomposition. Unlike traditional image-based methods which uniformly process all grids, our method saves time in the easy area and concentrates on refining the "difficult" area, e.g., the highly twisted area. Moreover, our method is robust in terms of shape description, e.g., less sensitive to boundary noise.

Simulating Group Behaviors [ publications ]

Adaptive Roadmap Based Group Behaviors
Group behavior can be observed everywhere. For example, birds fly in flocks, fish swim in schools, sheep move as a herd steered by a dog, and ants explore until they find a food source and then all ants follow the same path to the food source. The objective of our research is to develop efficient techniques for simulating such behaviors. In our research, we integrate adaptive roadmaps with traditional flocking techniques to generate complex global behaviors that are difficult to generate using traditional emergent approaches such as flocking. An adaptive roadmap is a roadmap (graph) containing representative paths in the environment whose edge values can be updated according to information gathered by the flock members.

Shepherding Behaviors
Shepherding behaviors are one class of flocking behaviors in which one or more external agents (called shepherds) attempt to control the motion of another group of agents (called flock) by exerting repulsive forces from shepherds to the flock. Shepherding behaviors can be found in various forms in nature. For example, herding, covering, patrolling and collecting are common types of shepherding behaviors.

Composable Group Behaviors
In this research, we investigate techniques to ease the process of generating realistic and complex group behaviors. Moreover, we provide a ``learning'' framework that allows these behaviors to adapt to new environments and new tasks and that allows users to make modifications easily.

Motion Planning and its Applications [ publications ]

A General Framework for Sampling on the Medial Axis of the Free Space
We propose a general framework for sampling the configuration space in which randomly generated configurations, free or not, are retracted onto the medial axis of the free space. In particular, our framework supports methods that retract a given configuration exactly or approximately onto the medial axis.

Motion Planning for Deformable Objects
In this work, we investigate methods for motion planning for deformable robots. Our framework is based on a probabilistic roadmap planner. We propose a two-stage approach. First an "approximate" path which might contain collisions is found. Next, we attempt to correct any collisions on this path by deforming the robot. We propose and analyze two methods for performing the deformations. Both techniques are inspired by physically correct behavior, but are more effcient than completely physically correct methods. Our approach can be applied in several domains, including flexible robots, computer modelling and animation, and biological simulations.

An Obstacle-Based Rapidly-Exploring Random Tree
We present modifications that can be made to the Rapidly-Exploring Random Tree (RRT) path planning algorithm that allow it to explore narrow passages or difficult areas more effectively. We show that along with using workspace obstacle information, C-space information can also be used when deciding which direction to grow. This planner works best in difficult areas when planning for free flying rigid or articulated robots. Where as the standard RRT can have problems planning in a narrow passage, the tree based planner presented here works best in these narrow or difficult areas.

Incremental Map Generation
One of the most important issues is the difficulty of deciding what size roadmap is required to solve a given problem efficiently. PRMs do not provide an automated way to determine appropriate roadmap size. In this paper, we propose a new PRM-based framework called Incremental Map Generation (IMG) to address this problem. Our strategy is to break the map generation into independent processes. Each process generates an independent roadmap component. IMG proceeds by adding independent roadmap components to an existing roadmap until some user defined criteria are satisfied.

Vizmo++: A visualization/authoring tool for motion planning
Vizmo++ is a 3D visualization/authoring tool for files provided/generated by OBPRM motion planning library. Vizmo++ is developed to offer a nice and easy to use interface that allows uses to display workspace environments, roadmap, path, and start/goal positions. It also allows users to interact with the environment by rotating obstacles and robots, changing the status of an object, taking pictures/movies, displaying an animation, to just name a few.

Navigating Through Virtual World
After the introduction of VRML, 3D web browsing has become a popular form of networked virtual reality. However, it is still a great challenge for a novice user equipped with a regular desktop PC to navigate in most virtual worlds of moderate complexity. We consider an alternative metaphor of allowing a user to specify locations of interests on a 2D-layout map and let the system automatically generate the animation of guided tours in virtual architectural environments. We describe an auto-navigation system, in which several efficient path-planning algorithms adapted from robotics are used. This system has been implemented in Java and adopts common VRML browsers as its 3D interface.

Scientific Computing [ publications ]

Seismic Ray Tracing
This project involves both Computer Science and Geo Sciences. Seismic Ray Tracing is often used to get information about the Earth's interior. It has applications in a number of areas in Geosciences including oil exploration and quake fault analysis. In Computer Science side, we are interested in several challenging geometric problems found in simulation of tracing seismic rays and in analysis of the traced rays.

Mapping Cortical Network
The brain has extraordinary computational power to represent and interpret complex natural environments. These natural computations are essentially determined by the topology and geometry of the brain's architectures. We present a framework to construct a 3D model of a cortical network using probabilistic roadmap methods. Our ultimate goal is to map and understand the connectivity and geometry of the cortical network.

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