The main goal of intelligent camera path planning is to determine an optimal pathway that proceeds from the starting position to the target position under several constraint conditions in the given environment. Genetic algorithm-based method has found wide application in path optimization problem in the intelligent camera community recently. Because the roaming environments are very complex, the planning path of the intelligent camera should meet other constraint conditions in addition to the path length constraint and the obstacle-free constraint. In this study, a new fitness function was developed in the genetic algorithm, which can consider the constraint conditions in terms of free obstacle, path length, path smoothness, and the visibility of the objective of interest in advance during the camera roaming. In addition, a new evolving operator was introduced into the genetic algorithm, so that the number of iteration can be significantly reduced, and thus, the efficiency of the genetic algorithm can be improved. Experimental results show that the proposed genetic algorithm can obtain a high-quality path under multi-constraint conditions for intelligent camera with less numbers of iteration as compared with several conventional methods.

智能相机路径规划的主要目标是确定在给定环境中的多个约束条件下，从起始位置到目标位置的最佳路径。基于遗传算法的方法最近在智能相机社区的路径优化问题中得到了广泛的应用。由于漫游环境非常复杂，因此智能相机的规划路径除了路径长度约束和无障碍约束外，还应满足其他约束条件。在这项研究中，在遗传算法中开发了一种新的适应度函数，该函数可以在相机漫游过程中预先考虑自由障碍物，路径长度，路径平滑度和感兴趣目标的可见性方面的约束条件。此外，在遗传算法中引入了新的进化算子，可以大大减少迭代次数，从而可以提高遗传算法的效率。实验结果表明，与几种常规方法相比，该遗传算法可以在多约束条件下获得智能相机的高质量路径，且迭代次数较少。