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

TL;DR

This paper presents an on-board UAV system using ROS C++ and a depth camera for real-time object interception, eliminating the need for external tracking tools, and demonstrates its effectiveness through simulation.

Contribution

It introduces a novel on-board control architecture with an iterative trajectory prediction algorithm and evaluates various path planning methods for object interception.

Findings

Successful simulation of object interception scenarios

Effective on-board trajectory prediction for non-propelled objects

Potential for autonomous UAV object interception without external aid

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 on-board autonomy of UAVs intercepting objects.