Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) is a monthly peer-reviewed scopus indexed journal originally founded in 1986. It is sponsored by the Ministry of Education, china and Northeastern University, china. Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) has always adhered to the correct purpose of running the journal, and has been committed to gathering and disseminating excellent academic achievements, inspiring technological innovation, and promoting the development of disciplines in my country.Aiming at major national needs and international frontiers, this journal has published a large number of original and high-level research result. The journal was selected into the "China Science and Technology Journal Excellence Action Plan Project" in December 2019.In the future, it will strive to build an open innovation, collaborative integration.
The calibration of kinematic parameters has been widely used to improve the pose (position and orientation) accuracy of the robot arm. Intelligent measuring equipment with high accuracy is usually provided for the industrial manipulator. Unfortunately, large noise exists in the vision measurement system, which is provided for space manipulators. To overcome the adverse effect of measuring noise and improve the optimality of calibrating time, a calibration method based on extended Kalman filter (EKF) for space manipulators is proposed in this paper. Firstly, the identification model based on
Given that the rapidly exploring random tree algorithm (RRT) and its variants cannot efficiently solve problems of path planning of autonomous vehicles, this paper proposes a new, adaptive improved RRT algorithm. Firstly, an adaptive directional sampling strategy is introduced to avoid excessive search by reducing the randomness of sampling points. Secondly, a reasonable node selection strategy is used to improve the smoothness of the path by utilizing a comprehensive criterion that combines angle and distance. Thirdly, an adaptive node expansion strategy is utilized to avoid invalid expans
Smartphones have emerged as a revolutionary technology for monitoring everyday life, and they have played an important role in Human Activity Recognition (HAR) due to its ubiquity. The sensors embedded in these devices allows recognizing human behaviors using machine learning techniques. However, not all solutions are feasible for implementation in smartphones, mainly because of its high computational cost. In this context, the proposed method, called HAR-SR, introduces information theory quantifiers as new features extracted from sensors data to create simple activity classification models
Aiming at the problem that the standard RRT algorithm (rapidly exploring random tree) using the pseudo-random sequence leads to uneven and unreasonable distribution of sampling points, and there are redundant sections and redundant nodes in the path of mobile robots from the starting point to the target point, a halton & dijkstra & rapidly exploring random tree(HDRRT) algorithm is proposed, which uses the Halton sequence with good uniform distribution of sampling points for sampling, and uses the candidate point set strategy to filter nodes to eliminate redundant nodes. At the same
As one of the core issues of autonomous vehicles, vehicle motion control directly affects vehicle safety and user experience. Therefore, it is expected to design a simple, reliable, and robust path following the controller that can handle complex situations. To deal with the longitudinal motion control problem, a speed tracking controller based on sliding mode control with nonlinear conditional integrator is proposed, and its stability is proved by the Lyapunov theory. Then, a linear parameter varying model predictive control (LPV-MPC) based lateral controller is formulated that the optimiz