Increasing environmental issues and energy crises led to rapid developments of hybrid electric vehicles, especially the planetary hybrid powertrain system (PHPS). This paper presents a comprehensive review of the PHPS, focusing primarily on contributions in the aspect of configuration, classification and comparison. In this work, a new classification method for PHPS architectures is proposed according to the number of electric motors (EMs). In addition, two kinds of PHPS, in the new classification framework, are extensively emphasized in terms of its architectures, advantages and disadvantages. Furthermore, the port diagrams of representative architectures are presented to provide an intuitive method for power flow representation. Finally, a conclusion is made to provide an insight for developing PHPS as well.

In order to solve the problems of low accuracy and low efficiency of point cloud registration for stereo camera systems, we propose a binocular stereo camera point cloud registration method based on IWOA and Improved ICP. We propose the following approaches in this paper—the registration process is divided into two steps to complete the initial coarse registration and the exact registration. In the initial registration stage, an improved Whale Optimization Algorithm (IWOA) based on nonlinear convergence factor and adaptive weight coefficients was proposed to realize the initial registration in combination with the RANSAC algorithm, and the obtained transformation matrix was used as the initial estimate of the subsequent exact registration algorithm. In the second step of the exact registration stage, an IICP algorithm with the introduction of normal vector weighting constraints at key points was proposed for achieving point cloud exact registration. This algorithm was verified by using Stanford point clouds (bunnies and monkeys) and our own point clouds algorithm, and the proposed algorithm in this paper has high registration accuracy, improved registration speed, and convergence speed.

To enhance the resolution timeliness and accuracy of inverse kinematics of industrial robots, a robot inverse kinematics method based on ELM-SSA-SCA was proposed. The positive kinematic model of the mechanical arm with Six Degrees of Freedom was established using D-H method, and ELM (extreme learning machine) with fast training speed was used to predict the initial resolution of robot inverse kinematics. The blended optimization method of SSA (Sparrow Search Algorithm) and SCA (Sine Cosine Algorithm) was applied to optimize the obtained initial inverse solution. The position under the optimum fitness was used as the output, so as to obtain the optimum solution of inverse kinematics. It is shown from the experimental results that the algorithm based on SSA-SCA-ELM achieved inverse solutions with faster convergence rate, higher precision and better timeliness compared to the solution method based on PSA-BP neural network.

To improve the accurate and sufficient recognition of abnormal points on the workpiece, a multidimensional anomaly point identification approach based on an improved eigenvalue method is proposed in this paper. Whether a point is normal or not depends on the angle between the two adjacent vectors which consisted of four adjacent points around the current focus. The comprehensive judgment is carried out by multidimensional approximation. The numerical simulation and actual experiment validate the efficiency of the proposed method to quickly and accurately identify the abnormal point cloud in the surface point cloud data.