Regeneration and Joining of the Learned Motion Primitives for Automated Vehicle Motion Planning Applications How to integrate human factors into the motion planning system is of great significance for improving the acceptance of intelligent vehicles. Decomposing motion into primitives and then accurately and smoothly joining the motion primitives (MPs) is an essential issue in the motion planning system. Therefore, the purpose of this paper is to regenerate and join the learned MPs in the library. By applying a representation algorithm based on the modified dynamic movement primitives (DMPs) and singular value decomposition (SVD), our method separates the basic shape parameters and finetuning shape parameters from the same type of demonstration trajectories in the MP library. Moreover, we convert the MP joining problem into a rerepresentation problem and use the characteristics of the proposed representation algorithm to achieve an accurate and smooth transition. This paper demonstrates that the proposed method can effectively reduce the number of shape adjustment parameters when the MPs are regenerated without affecting the accuracy of the representation. Besides, we also present the ability of the proposed method to smooth the velocity jump when the MPs are connected and evaluate its effect on the accuracy of tracking the set target points. The results show that the proposed method can not only improve the adjustment ability of a single MP in response to different motion planning requirements but also meet the basic requirements of MP joining in the generation of MP sequences.

Learning ViaPoint Movement Primitives with Inter and Extrapolation Capabilities Movement Primitives (MPs) are a promising way for representing robot motions in a flexible and adaptable manner. Due to the simple and compact form, they have been widely used in robotics. A major goal of the research activities on MPs is to learn models, which can adapt to changing task constraints, e.g. new motion targets. However, the adaptability of current MPs is limited to a small set of constraints due to their simple structures. It is indeed not a trivial task to maintain the simplicity of MPs representation and, at the same time, enhance their adaptability. In this paper, we discuss the adaptability of popular MPs such as Dynamic Movement Primitives (DMP) and Probabilistic Movement Primitives (ProMP) and propose a new simple but efficient formulation of MPs, the Viapoints Movement Primitive (VMP), that can adapt to arbitrary viapoints using a simple structured model that is based on the previous approaches but outperforms those in terms of extrapolation abilities.