Extracting camera-control requirements and camera movement generation in a 3D virtual environment

This paper proposes a new method to generate smooth camera movement that is collision-free in a three-dimensional virtual environment. It generates a set of cells based on cell decomposition using a loose octree in order not to intersect with polygons of the environment. The method defines a camera movement space (also known as Configuration Space) which is a set of cells in the virtual environment. In order to generate collision-free camera movement, the method holds a path as a graph structure which is based on the adjacency relationship of the cells, and makes the camera move on the graph. Furthermore, by using a potential function for finding out the force that aims the camera at the subject and a penalty function for finding out the force that restrains the camera on the graph when the camera moves on the graph, we generate smooth camera movement that captures the subject while avoiding obstacles. Several results in static and dynamic environments are presented and discussed.

[1]  Günther Schmidt,et al.  Integrated mobile robot motion planning and execution in changing indoor environments , 1994, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94).

[2]  Steven M. LaValle,et al.  RRT-connect: An efficient approach to single-query path planning , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[3]  B. Faverjon,et al.  Probabilistic Roadmaps for Path Planning in High-Dimensional Con(cid:12)guration Spaces , 1996 .

[4]  Nancy M. Amato,et al.  Probabilistic roadmaps-putting it all together , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[5]  Martin Herman,et al.  Fast, three-dimensional, collision-free motion planning , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[6]  Steven M. LaValle,et al.  On the Relationship between Classical Grid Search and Probabilistic Roadmaps , 2004, Int. J. Robotics Res..

[7]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[8]  Charles W. Warren,et al.  Fast path planning using modified A* method , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[9]  Gino van den Bergen Efficient Collision Detection of Complex Deformable Models using AABB Trees , 1997, J. Graphics, GPU, & Game Tools.

[10]  Carlos Vázquez,et al.  C-space decomposition using deterministic sampling and distance , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Lydia E. Kavraki,et al.  Probabilistic roadmaps for path planning in high-dimensional configuration spaces , 1996, IEEE Trans. Robotics Autom..