Sparse Gaussian processes for multi-step motion prediction of space tumbling objects

Abstract

Since the robotic capturing of a space tumbling object requires a rapidly accurate prediction of the object motion, this paper addresses the nonlinear multi-step motion prediction issue of the object based on machine learning theory and proposes a novel prediction approach, in which the prediction efficiency and the prediction accuracy are main focuses. To improve the prediction efficiency, sparse Gaussian processes are employed for the multi-step motion prediction, only providing the historical data of the object motion states. The sparse pseudo training data continuously optimized from real historical data makes it feasible for real-time predictions. To improve the prediction accuracy, a heuristic optimizer, Markov chain Monte Carlo, is integrated within sparse Gaussian processes to overcome the sensitivity problem caused by random initializations during the training process, thus avoids the local optimal training results. Then, a high-quality sparse pseudo training data is obtained to construct the predictive distributions during the testing process. The benchmark example and comparisons of several motion prediction applications illustrate the correctness and effectiveness of the proposed approach, and the experimental validation demonstrates its real-world potential.