This use case chapter presents a set of algorithms for the problems of autonomous exploration, terrain monitoring and optimized inspection path planning using aerial robots. The autonomous exploration algorithms described employ a receding horizon structure to iteratively derive the action that the robot should take to optimally explore its environment when no prior map is available, with the extension to localization uncertainty–aware planning. Terrain monitoring is tackled by a finite–horizon informative planning algorithm that further respects time budget limitations. For the problem of optimized inspection with a model of the environment known a priori, an offline path planning algorithm is proposed. All methods proposed are characterized by computational efficiency and have been tested thoroughly via multiple experiments. The Robot Operating System corresponds to the common middleware for the outlined family of methods. By the end of this chapter, the reader should be able to use the open–source contributions of the algorithms presented, implement them from scratch, or modify them to further fit the needs of a particular autonomous exploration, terrain monitoring, or structural inspection mission using aerial robots. Four different open–source ROS packages (compatible with ROS Indigo, Jade and Kinetic) are released, while the repository https://github.com/unr-arl/informative-planning stands as a single point of reference for all of them.