Kasemsit T., Hai-Ning W., Hae Won P.
Copy this from the other page
- R. Tedrake, "LQR-Trees: Feedback motion planning on sparse randomized trees" In Proceedings of Robotics: Science and Systems (RSS), page 8, 2009. - This paper …
- A. Nakashima, Y. Sugiyama, Y. Hayakawa, "Paddle Juggling by Robot Manipulator with Visual Servo" Control, Automation, Robotics and Vision, 2006.
- R. Burridge, A. Rizzi, D. Koditschek, "Sequential Composition of Dynamically Dexterous Robot Behaviors" The International Journal of Robotics Research, 1999
In as much detail as possible, describe your proposed work. Separate this description into the planning (autonomous decision making) challenges and others. Make sure to include both the problems you will be solving and the methods you expect to use to solve those problems.
- Planning Challenges
- What about your problem makes it interesting from the perspective of planning. What challenges will you encounter that basic planners / motion planners / controllers do not address.
- Proposed Solutions
- How will your work address these challenges. Be as specific as possible
- Implementation Challenges
- What other problems do you expect to face in your implementation? Will you need some kind of robot hardware / software / sensors? Will you need to solve any problems in addition to planning.
- Proposed Solutions
- What algorithms will be required to make these tools useful to your planner? How will you acquire them / implement them?
|Week 1||Write this document. Create a presentation for the class. Split work|
|Week 2||Work on Project 2|
|Week 3||Detailed brainstorming on LQR-tree / setup Simulation Environment|
|Week 4||Trajectory Tracking / Angle Control implementation|
|Week 5||Trajectory Tracking / Angle Control implementation|
|Week 6||Combine with Simulation|
|Week 8||Final Presentation / Report|
<Be able to answer theses questions>
How can LQR-tree be applied to trajectory planning?
How should we control the angle of the paddle?
How should we control the force of the paddle? (might be included in trajectory planning)