Observing and Forecasting the Trajectory of the Thrown Body with use of Genetic Programming

Abstract

Robotic catching of thrown objects is one of the common robotic tasks, which is explored in a number of papers. This task includes subtask of tracking and forecasting the trajectory of the thrown object. Here we propose an algorithm for estimating future trajectory based on video signal from two cameras. Most of existing implementations use deterministic trajectory prediction and several are based on machine learning. We propose a combined forecasting algorithm where the deterministic motion model for each trajectory is generated via the genetic programming algorithm. Object trajectory is extracted from video sequence by the image processing algorithm, which include Canny edge detection, Random Sample Consensus circle recognition and stereo triangulation. After that rajectory is forecasted using proposed method. Numerical experiments with real trajectories of the thrown tennis ball show that the algorithm is able to forecast the trajectory accurately.