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.

Visual–tactile fusion information plays a crucial role in robotic object classification. The fusion module in existing visual–tactile fusion models directly splices visual and tactile features at the feature layer; however, for different objects, the contributions of visual features and tactile features to classification are different. Moreover, direct concatenation may ignore features that are more beneficial for classification and will also increase computational costs and reduce model classification efficiency. To utilize object feature information more effectively and further improve the efficiency and accuracy of robotic object classification, we propose a visual–tactile fusion object classification method based on adaptive feature weighting in this article. First, a lightweight feature extraction module is used to extract the visual and tactile features of each object. Then, the two feature vectors are input into an adaptive weighted fusion module. Finally, the fused feature vector is input into the fully connected layer for classification, yielding the categories and physical attributes of the objects. In this article, extensive experiments are performed with the Penn Haptic Adjective Corpus 2 public dataset and the newly developed Visual-Haptic Adjective Corpus 52 dataset. The experimental results demonstrate that for the public dataset Penn Haptic Adjective Corpus 2, our method achieves a value of 0.9750 in terms of the area under the curve. Compared with the highest area under the curve obtained by the existing state-of-the-art methods, our method improves by 1.92%. Moreover, compared with the existing state-of-the-art methods, our method achieves the best results in training time and inference time; while for the novel Visual-Haptic Adjective Corpus 52 dataset, our method achieves values of 0.9827 and 0.9850 in terms of the area under the curve and accuracy metrics, respectively. Furthermore, the inference time reaches 1.559 s/sheet, demonstrating the effectiveness of the proposed method.

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.

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.

Aiming at the shortcomings of the existing machine vision pose measurement technology, a pose measurement method based on monocular vision and a cooperative target is proposed. A planar target designed with circles and rings as the main body is dedicated to object pose measurement, and a feature point coordinate extraction and sorting algorithm is designed for this target to effectively extract image features on the target. The RANSAC algorithm and topology-based fitting for the intersection method are used to optimise data processing, further improving the accuracy of feature point coordinate extraction and ultimately achieving high-precision measurement of object poses. The experimental results show that the measurement accuracy of the roll angle perpendicular to the optical axis can reach 0.02°, and the repeatability can reach 0.0004° after removing the systematic error; the measurement accuracy of the pitch angle can reach 0.03°, and the repeatability can go to 0.002° after removing the systematic error. The measurement range of the pitch angle is [−30°, +30°]; the measurement range of the roll angle is [−179°, +179°]. The experimental results show that the system has high measurement accuracy and meets the requirements of high-precision measurement.

Light pens for 3D vision coordinate measurement systems are increasingly widely used due to their advantages, such as their small size, convenience of being carried, and widespread applicability. The posture of the light pen is an important factor that affects accuracy. The pose domain of the pen needs to be given so that the measurement system has a suitable measurement range to obtain more qualified parameters. The advantage of the self-calibration method is that the entire self-calibration process can be completed at the measurement site with no auxiliary equipment. After the system camera calibration was completed, we took several pictures of the same measurement point with different poses to obtain the conversion matrix of the picture and subsequently used spherical fitting, the generalized inverse method of least squares, and the principle of position invariance in the pose domain range. The combined stylus tip center self-calibration method calculates the actual position of the light pen probe. The experimental results verify the effectiveness of the method; the measurement accuracy of the system can satisfy basic industrial measurement requirements.