Choosing the best path during unmanned air vehicle （UAV） flying is the target of the UAV mission planning problem. Because of its nearly constant flight height, the UAV mission planning problem can be treated as a 2-D （horizontal） path arrangement problem. By modeling the antiaircraft threat, the UAV mission planning can be mapped to the traveling seaman problem （TSP）. A new algorithm is presented to solve the TSP. The algorithm combines the traditional ant colony system （ACS） with particle swarm optimization （PSO）, thus being called the AC-PSO algorithm. It uses one by one tour building strategy like ACS to determine that the target point can be chosen like PSO. Experiments show that AC-PSO synthesizes both ACS and PSO and obtains excellent solution of the UAV mission planning with a higher accuracy.
An efficient voxelization algorithm is presented for polygonal models by using the hardware support for the 2 D rasterization algorithm and the GPU programmable function to satisfy the volumetric display system. The volume is sampled into slices by the rendering hardware and then slices are rasterated into a series of voxels. A composed buffer is used to record encoded voxels of the target volume to reduce the graphic memory requirement. In the algorithm, dynamic vertexes and index buffers are used to improve the voxelization efficiency. Experimental results show that the algorithm is efficient for a true 3-D display system.
