Aiming to resolve the problems of slow convergence speed and inability to plan in real time when ant colony optimization (ACO) performs global path planning, we propose a path-planning method that improves adaptive ant colony optimization (IAACO) with the dynamic window approach (DWA). Firstly, the heuristic information function is modified, and the adaptive adjustment factor is added to speed up the algorithm’s convergence rate; secondly, elite ants and max–min ants systems are implemented to enhance the global pheromone updating process, and an adaptive pheromone volatilization factor is aimed at preventing the algorithm from enhancing its global search capabilities; then, the path optimization and withdrawal mechanism is utilized to enable smoother functioning and to avoid the deadlocks; finally, a new distance function is introduced in the evaluation function of DWA to the enhance real-time obstacle-avoidance ability. The simulation experiment results reveal that the path length of the IAACO can be shortened by 10.1% and 13.7% in contrast to the ACO. The iteration count can be decreased by 63.3% and 63.0%, respectively, leading to an enhanced optimization performance in global path planning and achieving dynamic real-time obstacle avoidance for local path planning.

The collected data of a pumping unit contain environmental noise, which significantly reduces the precision of fault diagnosis. The previous fault detection approach depends on manual feature extraction, which is time-consuming and laborious, and it cannot cope with high-noise conditions. Therefore, we propose a dual-branch time–frequency fusion deep learning model for fault diagnosis of the pumping unit. One branch extracts time-domain information, while the other branch extracts frequency-domain information by employing the fast Fourier transform. The branch information of these two branches is concatenated, and the gate-controlled channel transfer unit module automatically learns the competitive and cooperative relationships between each branch, making the key features more prominent in information fusion. Consequently, an accurate fault diagnosis of the pumping unit can be achieved under high-noise conditions. The results demonstrate that the proposed model outperforms the traditional schemes in terms of noise, with different signal-to-noise ratios.

With the development of vision and haptic sensor technologies, robots have become increasingly capable of perceiving their external environments. Although machine vision and haptics have surpassed humans in some aspects of perception, it is difficult for robots to describe objects from multiple viewpoints by using a combination of visual and haptic modalities. In this paper, we use convolutional neural networks to separately extract visual and haptic features and then fuse these two types of features. Then, multitask learning is combined with multilabel classification to form a multitask-multilabel classification method. The developed method is used to identify the color, shape, material attributes, and class of an object from the visual-haptic fused feature vector. To verify the effectiveness of the proposed object description method, experiments are conducted on the PHAC-2 dataset and the collected VHAC dataset. The experimental results show that the proposed method produces the most accurate object descriptions with the smallest number of parameters.

This manuscript surveys and analyses the different implementations of knowledge-based systems (KBS) in sheet metal workings. The scope will cover different stamping phases; specifically the stamped part design, the stamping process planning, the die structure planning and manufacturing with the focus on progressive die designs, and lastly, the strip layout planning. The analysis of the KBS in each of these stamping phases will include the intended function of the system, the KBS construction methodology and the reasoning logic scheme utilised to manipulate the content of the knowledge bases. The survey compares the followed approaches in terms of challenges and merits found for each followed approach. This work serves as a current state-of-the-art analysis for employing KBS in sheet metal stamping (SMS).

In this paper, we describe a position sensorless vector control system for a permanent magnet synchronous motor (PMSM) for a lawnmower in order to solve the problems of inferior dynamic performance and insufficient load resistance in the control process of lawnmower motors. A speed–current double-closed-loop vector control strategy was adopted to control the motor speed; an extended Kalman filter (EKF) was constructed to track the motor rotor position. STM32F407 was selected as the main control chip to establish the hardware experimental platform, and the performance of the control system was evaluated. The experimental results demonstrate that the control system accurately regulates motor speed, has good dynamic response characteristics, and can maintain stability under various loads; therefore, it meets the performance requirements of lawnmower motors in practical operation.

Light-pen coordinate measuring machines are widely used due to their portability; however, the measurement accuracy is affected by camera calibration, light-pen probe calibration, control points, etc. To study the influence of control point dispersion on measurement accuracy, a light-pen coordinate measuring system is constructed in this paper. In this system, a new type of control point distribution is designed. Based on this distribution, five light-pens with different control point dispersions are created and an algorithm is proposed to match control points with their images. The experimental repeatability results for the five light-pen probes indicate satisfactory repeatability, which ensures the effectiveness of the measurement results. The experimental measurement results for five light-pens demonstrate that the measurement error decreases with the decline of the control point dispersion at first, but begins to increase when the control point dispersion reaches a certain value.