In recent years, deep learning has been widely used in emotion recognition, but the models and algorithms in practical applications still have much room for improvement. With the development of graph convolutional neural networks, new ideas for emotional recognition based on EEG have arisen. In this paper, we propose a novel deep learning model-based emotion recognition method. First, the EEG signal is spatially filtered by using the common spatial pattern (CSP), and the filtered signal is converted into a time–frequency map by continuous wavelet transform (CWT). This is used as the input data of the network; then the feature extraction and classification are performed by the deep learning model. We called this model CNN-BiLSTM-MHSA, which consists of a convolutional neural network (CNN), bi-directional long and short-term memory network (BiLSTM), and multi-head self-attention (MHSA). This network is capable of learning the time series and spatial information of EEG emotion signals in depth, smoothing EEG signals and extracting deep features with CNN, learning emotion information of future and past time series with BiLSTM, and improving recognition accuracy with MHSA by reassigning weights to emotion features. Finally, we conducted experiments on the DEAP dataset for sentiment classification, and the experimental results showed that the method has better results than the existing classification. The accuracy of high and low valence, arousal, dominance, and liking state recognition is 98.10%, and the accuracy of four classifications of high and low valence-arousal recognition is 89.33%.

With the rise of robotics within various fields, there has been a significant development in the use of mobile robots. For mobile robots performing unmanned delivery tasks, autonomous robot navigation based on complex environments is particularly important. In this paper, an improved Gray Wolf Optimization (GWO)-based algorithm is proposed to realize the autonomous path planning of mobile robots in complex scenarios. First, the strategy for generating the initial wolf pack of the GWO algorithm is modified by introducing a two-dimensional Tent–Sine coupled chaotic mapping in this paper. This guarantees that the GWO algorithm generates the initial population diversity while improving the randomness between the two-dimensional state variables of the path nodes. Second, by introducing the opposition-based learning method based on the elite strategy, the adaptive nonlinear inertia weight strategy and random wandering law of the Butterfly Optimization Algorithm (BOA), this paper improves the defects of slow convergence speed, low accuracy, and imbalance between global exploration and local mining functions of the GWO algorithm in dealing with high-dimensional complex problems. In this paper, the improved algorithm is named as an EWB-GWO algorithm, where EWB is the abbreviation of three strategies. Finally, this paper enhances the rationalization of the initial population generation of the EWB-GWO algorithm based on the visual-field line detection technique of Bresenham’s line algorithm, reduces the number of iterations of the EWB-GWO algorithm, and decreases the time complexity of the algorithm in dealing with the path planning problem. The simulation results show that the EWB-GWO algorithm is very competitive among metaheuristics of the same type. It also achieves optimal path length measures and smoothness metrics in the path planning experiments.

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Simultaneous Localization and Mapping (SLAM) plays an irreplaceable role in the field of artificial intelligence. The traditional visual SLAM algorithm is stable assuming a static environment, but has lower robustness and accuracy in dynamic scenes, which affects its localization accuracy. To address this problem, a semantic SLAM system is proposed that incorporates ORB-SLAM3, semantic segmentation thread and geometric thread, namely DeepLabv3 + _SLAM. The improved DeepLabv3 + semantic segmentation network combines context information to segment potential a priori dynamic objects. Then, the geometry thread uses a multi-view geometry method to detect the motion state information of the dynamic object. Finally, a new ant colony strategy is proposed to find the group of all dynamic feature points through the optimal path, and avoids traversing all the feature points to reduce the dynamic object detection time and improve the real-time performance of the system. By conducting experiments on public data sets, the results show that the method proposed in this paper effectively improves the positioning accuracy of the system in a high-dynamic environment compared with similar algorithms, and the real-time performance of the system is improved.INDEX TERMS DeepLabv3 + _SLAM, semantic, high-dynamic environment, new ant colony strategy.