Considering the complexity of the three-dimensional environment and the flexibility of the quadrotor aircraft, using the traditional A* algorithm for global path planning has the disadvantages of less search direction, more expanded nodes, and a longer planning path. Therefore, an improved A* algorithm is proposed, which is improved from two aspects. Firstly, a two-layer extended neighborhood strategy is proposed, which can increase the search direction and make better use of the flexibility of the aircraft. Secondly, the heuristic function is improved to make the heuristic function value closer to the actual planning path distance, which can reduce the expansion nodes and optimize the planning path. Finally, the path planning simulation of the improved A* algorithm is carried out and the results show that the path planned by the improved algorithm is shorter and the expanded nodes are fewer, which can guide the quadrotor to reach the destination better.

This study obtains the analytical inverse system of a permanent magnet synchronous motor (PMSM) model based on the traditional magnetic field orientation decoupling control mode by analyzing the inverse quality of the PMSM. Using the neural network’s excellent approximation ability and well learning functions, a neural network inverse system (NNIS) of the decoupling control system was established by identifying and offline training the back propagation neural network (BPNN) and radial basis function neural network (RBFNN). The data collected from the analytical inverse system of the PMSM model are used to analyze and compare the prediction accuracy and running time of the neural network, so as to optimize the structure and parameters of the neural network. The simulation results of three PMSM decoupling control systems show that the PMSM decoupling control system based on RBF NNIS has good dynamic and static decoupling performance, and robustness.

Herein, we report the γ-ray ionizing radiation response of a commercial monolithic active-pixel sensor (MAPS) camera under strong-dose-rate irradiation with an online detection and monitoring system for strong radiation conditions. We present the first results of the distribution of three types of MAPS camera and establish a linear relationship between the average response signal and radiation dose rate in the strong-dose-rate range. There is an obvious response signal in the video frames when the camera module parameters are set to automatic, but the linear response is very poor. However, the fixed image parameters are not good at adapting to the changes of the environment and affect the quality of the video frames. A dual module online radiation detection and monitoring probe was made to carry out effective video monitoring and radiation detection at the same time. The measurement results show that the dose rate detection error is less than 5% with a dose rate in the range of 60 to 425 Gy/h, and the visible light image does not have obvious distortion, deformation, or color shift due to the interference of the radiation response event and radiation damage. Hence, the system test results show that it can be used for online detection and monitoring in a strong radiation environment.

This paper proposes a novel adaptive dynamic programming (ADP) approach to address the optimal consensus control problem for discrete‐time multiagent systems (MASs). Compared with the traditional optimal control algorithms for MASs, the proposed algorithm is designed on the basis of the event‐triggered scheme which can save the communication and computation resources. First, the consensus tracking problem is transferred into the input‐state stable (ISS) problem. Based on this, the event‐triggered condition for each agent is designed and the event‐triggered ADP is presented. Second, neural networks are introduced to simplify the application of the proposed algorithm. Third, the stability analysis of the MASs under the event‐triggered conditions is provided and the estimate errors of the neural networks’ weights are also proved to be ultimately uniformly bounded. Finally, the simulation results demonstrate the effectiveness of the event‐triggered ADP consensus control method.

Customer segmentation is a core application of data mining in the retail industry. Traditional K-means clustering is widely adopted here for its simple principle and high computational efficiency, yet it has notable drawbacks: random initial clustering centers easily lead to local optimal solutions, it is highly sensitive to abnormal data, and the cluster number K relies on manual experience, resulting in unstable clustering performance. This paper designs an improved K-means algorithm, which filters outliers through a two-layer mechanism combining Local Outlier Factor and distance threshold. It also constructs a multi-index system with Silhouette Coefficient, Calinski-Harabasz and Davies-Bouldin indices to automatically determine the optimal K-value, optimizes initial centers via density peak clustering, and introduces weighted Euclidean distance to enhance clustering compactness. Experiments on the Mall Customer Segmentation dataset compare the proposed algorithm with traditional K-means, K-medoids and DBSCAN. Results show it achieves a Silhouette Coefficient of 0.5821, a CH index of 1025.36 and a DB index of 0.5107, outperforming all comparison algorithms in all indicators with more reasonable and stable clustering results. Applied to mall customer segmentation, this algorithm divides customers into 5 groups with distinct characteristics, providing solid data support for malls to formulate scientific and differentiated marketing strategies.

Aiming at the application requirements of artificial intelligence and robotics, this paper adopts a lightweight and low-cost scheme to construct a dancing-robot system integrating machine vision, mechanical structure and motion control so as to improve its accurate human-motion imitation capability. It provides an important reference for the engineering application of motion imitation of humanoid robots. A human-pose visual-recognition model is built based on Python and deep-learning techniques. A monocular camera captures two-dimensional images, from which skeletal key-point coordinates are extracted via 3D reconstruction. Joint angles are calculated by inverse kinematics to supply core input data for motion imitation. Mechanically, a humanoid joint structure composed of 16 servos is adopted to realize one-to-one mapping and execution of joint angles. In hardware, a collaborative control circuit is constructed with the main-control module and servo-drive module as the core. Software implements data-parsing, instruction generation and closed-loop control. After system integration and debugging, the robot can stably and accurately reproduce simple human dance movements, which verifies the feasibility and effectiveness of the proposed scheme.