The field of unmanned aerial vehicle, or UAV, navigation has been moving towards collision inclusive path planning, yet work has not been done to consider what a UAV is colliding with, and if it should or not. Therefore, there is a need for a framework that allows a UAV to consider what is around it and find the best collision candidate. The following work presents a framework that allows UAVs to do so, by considering what an object is and the properties associated with it. Specifically, it considers an object’s material and monetary value to decide if it is good to collide with or not. This information is then published on a binary occupancy map that contains the objects’ size and location with respect to the current position of the UAV. The intent is that the generated binary occupancy map can be used with a path planner to decide what the UAV should collide with. The framework was designed to be as modular as possible and to work with conventional UAV's that have some degree of crash resistance incorporated into their design. The framework was tested by using it to identify various objects that could be collision candidates or not, and then carrying out collisions with some of the objects to test the framework’s accuracy. The purpose of this research was to further the field of collision inclusive path planning by allowing UAVs to know, in a way, what they are intending to collide with and decide if they should or not in order to make safer and more efficient collisions.