Transportation of small objects by robotic throwing and catching: applying genetic programming for trajectory estimation

Abstract

Robotic catching of thrown objects is one of the common robotic tasks, which is explored in several works. 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. Genetic programming is implemented on C++ with use of CUDA library and executed in parallel way on the graphical processing unit. Parallel execution allow genetic programming in real time. Numerical experiments with real trajectories of the thrown tennis ball show that the algorithm can forecast the trajectory accurately. © 2016