원문정보
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초록
영어
The objective of this work is to propose two new algorithms for collision detection for real-time application. They are applicable to rigid objects enclosed in boxes in order to improve the time of collision detection. The proposed algorithms, called Neuro-SAT and perceptron learning with displacement of the base frame, will be compared with the algorithm Separating Axis Test (SAT) based on the hierarchy of the OBB tree. A grasping operation of an object, with a robotic hand, was executed to test the developed algorithms. The results, of simulation experiments, reveal a great improvement in the time of collision detection.
목차
Abstract
1. Introduction
2. Construction of the Hierarchy of the Bounding Volumes
3. The OBB Method
4. Notion of the Separating Axis
5. Improved of the Detection Collision Time by Changing the Base Frame
6. Triangle-Triangle Intersection Test
7. Control of the Manipulator Arms and Fingers
8. Linearization and Decoupling of the Dynamic Model
9. Simulation Results of the Collision Detection
10. Neuro-SAT Algorithm
11. Implementation of the Algorithms to a Hand and an Object Formed by Triangles
12. Conclusion
References
1. Introduction
2. Construction of the Hierarchy of the Bounding Volumes
3. The OBB Method
4. Notion of the Separating Axis
5. Improved of the Detection Collision Time by Changing the Base Frame
6. Triangle-Triangle Intersection Test
7. Control of the Manipulator Arms and Fingers
8. Linearization and Decoupling of the Dynamic Model
9. Simulation Results of the Collision Detection
10. Neuro-SAT Algorithm
11. Implementation of the Algorithms to a Hand and an Object Formed by Triangles
12. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보