Tsai-Yen Li

Assembly maintainability study with motion planning

Maintainability is an important issue in design where the accessibility of certain parts is determined for routine maintenance. In the past its study has been largely manual and labor intensive. Either by using physical mockup or computer animation with CAD models of a design, the task relies on a human to provide an access path for the part. In this paper, the authors present an automated approach to replace this manual process. By applying results from and developing extensions to research in motion planning and other fields, the authors demonstrate that an automated maintainability study system is feasible. The authors describe general extensions needed to adapt robotic motion planning techniques in maintainability studies. The authors show results from applying such a system to two classes of industrial application problems.

Motion planning for a crowd of robots

Moving a crowd of robots or avatars from their current configurations to some destination area without causing collisions is a challenging motion-planning problem because the high degrees of freedom involved. Two approaches are often used for this type of problems: decoupled and centralized. The tradeoff of these two approaches is that the decoupled approach is considered faster while the centralized approach has the advantage of being complete. In this paper, we propose an efficient centralized planner that is much faster than the traditional randomized planning approaches. This planner uses a hierarchical sphere tree structure to group robots dynamically. By taking advantage of the problem characteristics on independently moving robots, we are able to design a practical planner with the centralized approach when the number of robots is rather large. We use several simulation examples to demonstrate the efficiency and effectiveness of the planner.

An incremental learning approach to motion planning with roadmap management

Traditional approaches to the motion-planning problem can be classified into single-query and multiple-query problems with the tradeoffs on run-time computation cost and adaptability to environment changes. In this paper, we propose a novel approach to the problem that can learn incrementally on every planning query and effectively manage the learned roadmap as the process goes on. This planner is based on previous work on probabilistic roadmaps and uses a data structure, called reconfigurable random forest (RRF), which extends the rapidly-exploring random tree structure proposed in the literature. The planner can account for environmental changes while keeping the size of the roadmap small. The planner removes invalid nodes in the roadmap as the obstacle configurations change. It also uses a tree-pruning algorithm to trim RRF into a more concise representation. Our experiments show that the planner is flexible and efficient.

Automatically generating virtual guided tours

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 think the main problem is due to the fact that a user usually uses a 2D mouse to provide low-level navigation control but the display frame rate is not high enough for this servo loop. 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. Specifically, we aim to generate animations of customizable tour paths and its associated human/camera motions in an on-line manner according to high-level user inputs. 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. We also use the geometric model of our departmental building as an example to demonstrate the efficiency and effectiveness of the system.

On-line manipulation planning for two robot arms in a dynamic environment

In a dynamic and partially unpredictable environment, robot motion planning must be on-line. The planner receives a continuous flow of information about occurring events and generates new plans, while previously planned motions are being executed. This paper describes an on-line planner for two cooperating arms whose task is to grab parts on a conveyor belt and transfer them to their respective goals, while avoiding collision with obstacles. Parts arrive on the belt in random order, at any time. This scenario is typical of manufacturing cells serving machine-tools, assembling products, or packaging objects. The proposed approach breaks the overall planning problem into subproblems, each involving a low-dimensional configuration or configuration x time space, and orchestrates very fast primitives solving these subproblems. The resulting planner has been implemented and extensively tested both in a simulated environment and with a real dual-arm system. Its competitiveness has been evaluated against an oracle making (almost) the best decision at any one time. The planner compares extremely well.

Incremental 3D collision detection with hierarchical data structures

3D collision detection is the most time-consuming component of many geometric reasoning applications. Any improvements on the efficiency of the collision detection module may have a great impact on the overall performance of these applications. Most efficient collision detection algorithms in the literature use some sort of hierarchical bounding volumes, such as spheres or oriented bounding boxes, to reduce the number of calls to expensive collision checks between polygons. In this paper, we propose an incremental scheme that takes advantage of spatial coherence to improve the performance of this class of algorithms. Experiments have been conducted on a sphere-tree structure for several moving objects. Consistent improvements ranging from 70 to 90 percents were observed. These numbers are actually very close to the theoretical upper bound for such improvements. 1.1 Keywords

Motion planning for humanoid walking in a layered environment

Motion planning is one of the key capabilities for autonomous humanoid robots. Previous researches have focused on weight balancing, collision detection, and gait generation. Most planners either assume that the environment can be simplified to a 2D workspace or assume that the path is given. In this paper, we propose a motion planning system capable of generating both global and local motions for a humanoid robot in a layered or two and half dimensional environment. The planner can generate a gross motion that moves the humanoid vertically as well as horizontally to avoid obstacles in the environments. The gross motion is further realized by a local planner that determines the most efficient footsteps and locomotion over uneven terrain. If the local planner fails, the failure is feedback to the global planner to consider other alternative paths. The implemented humanoid planning system is an interactive tool that can compute collision-free motions for a humanoid robot in an online manner.

Simulating virtual human crowds with a leader-follower model

Although virtual humans have been an active research topic for many years, most research has focused on simulating various aspects of a humanoid instead of a crowd of people. We report our progress on generating collision free gross motions for multiple virtual crowds. Each virtual crowd consists of a leader and many followers. The leader is in charge of generating motions for its own group with the motions of other crowds taken into account. The followers use artificial life principles to follow the leader as it moves towards the goal. The high degrees of freedom involved in the crowd simulation system present a great challenge for gross motion planning. We adopt a decoupled path-planning approach, in which the paths being executed by other leaders become the motion constraint of the current leader under consideration. In addition, we take a unified view of search space to extend the planner to consider a whole crowd instead of just its leader. Experimental results show that our planner can efficiently generate satisfactory motions. We believe that such a planning system is a good addition to controlling groups of avatars in a 3D virtual environment.

An intelligent user interface with motion planning for 3D navigation

Due to the rapid evolution of graphics hardware, interactive 3D graphics is becoming popular on desktop personal computers. However, it remains a challenging task for a novice user equipped with a 2D mouse to navigate in an architectural environment efficiently. We think the problem is partly due to the fact that precise navigation control is difficult to achieve with low frame rates. In this paper, we propose a novel approach to improve the effectiveness and efficiency of 3D navigation for architectural walkthrough applications. We adopt a path planner with a probabilistic roadmap to help users avoid unnecessary maneuvers due to collisions with the environment. We modify a Java3D implementation of a VRML browser to incorporate the path planner into the user interface. Experiments show that our implementation of the path planner is very efficient and can be seamlessly incorporated into the navigation control loop. The overall navigation time for traversing a sequence of checkpoints in a maze-like environment can be improved by about a factor of two if the intelligent user interface is used.

Real-Time Camera Planning for Navigation in Virtual Environments

In this work, we have developed a real-time camera control module for navigation in virtual environments. With this module, the tracking motion of a third-person camera can be generated automatically to allow a user to focus on the control of an avatar. The core of this module consists of a motion planner that uses the probabilistic roadmap method and a lazy update strategy to generate the motion of the camera, possibly with necessary intercuts. A dynamic roadmap specified relative to the avatar is updated in real time within a time budget to account for occlusions in every frame of the control loop. In addition, the planner also allows a user to specify preferences on how the tracking motion is generated. We will use several examples to demonstrate the effectiveness of this real-time camera planning system.