Trajectory Prediction Path Planning for an Object Intercepting UAV with a Mounted Depth Camera

Abstract

A novel control software architecture using ROS C++ is introduced for object interception by a UAV with a mounted depth camera and no external aid. Existing work in trajectory prediction focused on the use of off-board tools like motion capture rooms to intercept thrown objects. The present study designs the UAV architecture to be completely on-board capable of object interception with the use of a depth camera and point cloud processing. The architecture uses an iterative trajectory prediction algorithm for non-propelled objects like a ping-pong ball. A variety of path planning approaches to object interception and their corresponding scenarios are discussed, evaluated simulated in Gazebo. The successful simulations exemplify the potential of using the proposed architecture for the onboard autonomy of UAVs intercepting objects.