# Virtual Reality Collision Detection Based on Improved Ant Colony Algorithm
## 6 Conclusions  
This study designed, implemented and verified a new collision detection algorithm with good performance, namely sphere-OBB-MACO, which is suitable for detecting collisions in complex scenes. The detection process of the algorithm is divided into two steps. In the preliminary detection stage, the spherical and OBB bounding boxes are selected, the binary tree is selected as the tree structure and the bounding volume hierarchy is constructed in a top-down manner. The tree's structure is traversed by the depth-first traversal method to quickly remove the triangular primitive pairs without collisions. In the accurate detection stage, the bounding boxes with collisions after preliminary detection are processed by the random sampling method, and the feature pairs are used to form the search space. The ant colony algorithm is introduced to solve the optimization problem of the search space. To overcome the problems of a slow convergence speed and the local  
Figure 5: The detection efficiency of four algorithms.  
optimal solution in the ant colony algorithm, the pheromone update rule of the ant colony algorithm has been optimized.  
In the fifth section, the superiority of the algorithm designed in this study was verified. The sphere-OBB-MACO algorithm designed in this study was more efficient in detecting collisions than traditional algorithms and some published algorithms in large-scale complex scenes, but it had some limitations as follows:  
1. Detecting collisions of flexible objects. The improved algorithm in this study is limited to rigid objects, but the detection of collisions between flexible objects is more difficult.
2. In a simple scenario, due to the small number of models and the simple structure, particle swarm optimization could be used to solve the optimization problem. For detecting collisions between convex polyhedrons, a faster genetic algorithm should be used to solve the optimization problem.  
Although this study has made some improvements to the collision detection algorithm and achieved certain results in terms of efficiency, there are still many problems that need to be further studied in the future as follows:  
1. Optimization of the complexity of the hierarchical tree's structure space. The sphere-OBB structure requires more storage space than a single bounding box structure. In the future, the storage space of the hierarchical tree needs to be optimized.
2. Optimization of ant colony algorithm. In this study, the pheromone update rules and the correlation coefficients of the ant colony algorithm were improved to optimize the ant colony algorithm. In the future, other intelligent algorithms could be introduced to optimize the ant colony algorithm.
3. In the future, the acceleration of computer hardware could be studied to further improve the efficiency of detection.  
Conceptualization, P.X.; methodology, P.X.; software, Q.S.; validation, P.X.; formal analysis, P.X.; investigation, Q.S.; resources, Q.S.; data curation, P.X.; writing\(-\)original draft preparation, P.X.; writing\(-\)review and editing, P.X.; visualization, P.X.; supervision, Q.S.; project administration, Q.S.; funding acquisition, Q.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.