A vehicle motion planning algorithm based on deep reinforcement learning was proposed to satisfy the efficiency and comfort requirements of intelligent connected vehicles at unsignalized intersections. Temporal convolutional network (TCN) and Transformer algorithms were combined to construct the intention prediction model for surrounding vehicles. The multi-layer convolution and self-attention mechanisms were used to improve the capability of capturing vehicle motion feature. The twin delayed deep deterministic policy gradient (TD3) reinforcement learning algorithm was employed to build the vehicle motion planning model. Taking the driving intention of surrounding vehicle, driving style, interaction risk, and the comfort of ego vehicle into consideration comprehensively, the state space and reward functions were designed to enhance understanding the dynamic environment. Delaying the policy updates and smoothing the target policies were conducted to improve the stability of the proposed algorithm, and the desired acceleration was output in real-time. Experimental results demonstrated that the proposed motion planning algorithm can perceive the real-time potential interaction risk based on the driving intention of surrounding vehicles. The generated motion planning strategy met the requirements of the efficiency, safety and comfort. It showed excellent adaptability to different styles of surrounding vehicles and dense interaction scenarios, and the success rates exceeded 92.1% in various scenarios.

Foundational models in natural language processing, computer vision and multimodal learning have achieved significant breakthroughs in recent years, showcasing the potential of general artificial intelligence. However, these models still fall short of human or animal intelligence in areas such as causal reasoning and understanding physical commonsense. This is because these models primarily rely on vast amounts of data and computational power, lacking direct interaction with and experiential learning from the real world. Many researchers are beginning to question whether merely scaling up model size is sufficient to address these fundamental issues. This has led the academic community to reevaluate the nature of intelligence, suggesting that intelligence arises not just from enhanced computational capabilities but from interactions with the environment. Embodied intelligence is gaining attention as it emphasizes that intelligent agents learn and adapt through direct interactions with the physical world, exhibiting characteristics closer to biological intelligence. A comprehensive survey of embodied artificial intelligence was provided in the context of foundational models. The underlying technical ideas, benchmarks, and applications of current embodied agents were discussed. A forward-looking analysis of future trends and challenges in embodied AI was offered.

A novel detection algorithm (efficient multi-scale attention (EMA) and small object detection based on YOLOv5, ES-YOLOv5) was proposed by targeting small ship targets in SAR scenes aiming at the issues of inconspicuous imaging features and low detection accuracy caused by arbitrary orientation of small targets in synthetic aperture radar (SAR) imaging. A small target detection layer was added to adjust the receptive field size, making it more suitable for capturing small target scale features and facilitating multi-scale fusion. An EMA mechanism was introduced to focus on key target information and enhance feature representation capability. The circular smooth label (CSL) technique was utilized to adapt to the periodicity of angles, achieving high-precision angle classification. The experimental results demonstrate that the proposed method achieves an average detection accuracy of 90.9% at an intersection over union (IoU) threshold of 0.5 on the RSDD-SAR dataset. The algorithm outperforms the baseline algorithm YOLOv5 by 6% in improving the precision of detecting small SAR ship targets, significantly enhancing the model’s detection performance.

A new estimation network was proposed for improving the insufficient occlusion handling ability of existing human pose estimation methods. An occluded parts enhanced convolutional network (OCNN) and an occluded features compensation graph convolutional network (OGCN) were included in the proposed network. A high-low order feature matching attention was designed to strengthen the occlusion area features, and high-adaptation weights were extracted by OCNN, achieving enhanced detection of the occluded parts with a small amount of occlusion data. OGCN strengthened the shared and private attribute compensation node features by eliminating the obstacle features. The adjacency matrix was importance-weighted to enhance the quality of the occlusion area features and to improve the detection accuracy. The proposed network achieved detection accuracy of 78.5%, 67.1%, and 77.8% in the datasets COCO2017, COCO-Wholebody, and CrowdPose, respectively, outperforming the comparative algorithms. The proposed network saved 75% of the training data usage in the self-built occlusion dataset.

The research on the influence of emotions on false memory helps explore the memory-processing mechanisms of the brain. The EEG signals of false memories under different emotion states were collected. The microstate analysis was used to obtain the template maps for each emotion group named from microstate 1 to microstate 5, the time segmentation of the four stages of memory recognition (early processing, familiar processing, episodic recall processing and post-extraction processing) for the emotion groups were divided according to the microstate fitting results, and the phase-locked brain functional networks were constructed in microstates with significant difference in time coverage. The results analyzed of EEG signals from both the temporal perspective and the spatial perspective show that the brain processing patterns of the emotion groups begin to appear different from the episode recall processing stage. The positive group remains in the active microstates 3 and 5 of the prefrontal region and has strong brain function, the negative group remains in microstate 1 and has poor brain function, and the neutral group remains in the active microstates 3 and 4 of the central region. The positive group spends more time and mental resources on plot association and reasoning, while the negative group stays depressed for a longer time, and the neutral group devotes more time and mental resources to information integration.

A multi-layer degradation module was proposed aiming at the problem that most remote sensing image super-resolution models rarely consider the impact of noise, blur, JPEG compression, and other factors on image reconstruction, as well as the limitations of Transformer modules in capturing high-frequency information. A CNN-Transformer hybrid network was designed, where CNN captures high-frequency details and Transformer extracts global information. These two components were combined by an attention-based aggregation module, enhancing local high-frequency detail reconstruction while maintaining global structural coherence. The model was tested on six random scenes from the AID dataset and compared with the MM-realSR model in PSNR and SSIM. Results show an average PSNR improvement of 1.61 dB and a SSIM increase of 0.023 over MM-realSR.

A new design scheme of crab-like hexapod origami robot was proposed by combining the origami structure with the multi-legged robot design and coupling Miura origami and six-fold origami aiming at the problems that the existing origami robots have a single structure and insufficient flexibility in movement. The motion configuration of the origami robot was expanded, and the motion flexibility of the origami robot was improved. Each leg of the robot has two degrees of freedom under the symmetry hypothesis. The vertices of the robot legs were treated as joints, and the crease lines were regarded as links. A planar link equivalent model of the robot legs was established with the folding angle as the motion variable. The theoretical range of motion for the robot’s foot was determined through simulation calculations. Then tapered panel technique was utilized to thicken the folding surfaces and prevent physical interference between adjacent folding surfaces. A three-dimensional model of the origami crab-like hexapod robot was constructed. The relationship between the folding angle and foot motion was analyzed based on the equivalent model of planar links, and the foot motion trajectory and gait of the robot were designed. The experimental prototype of origami bionic hexapod robot was designed and manufactured by using 3D printing technology, and the lateral movement of the robot was realized based on STM32 microcontroller control. Results show that the origami bio-inspired robot can realize the conversion from plane configuration to a crab-like configuration. The robot can move smoothly left and right under the coordinated movement of six legs.

An enhanced genetic algorithm was proposed to address the challenge of area coverage path planning for a tilt-rotor unmanned aerial vehicle (TRUAV) amidst multiple obstacles. A preliminary coverage path plan for the designated task area was devised, utilizing the minimum spanning and back-and-forth path generation algorithms. The area coverage dilemma was transformed into a traveling salesman problem to optimize the sequence of the coverage path. A fishtail-shaped obstacle avoidance strategy was proposed to circumvent obstacles within the region. The nearest neighbor algorithm was introduced to generate a superior initial population than a genetic algorithm. A three-point crossover operator and a dynamic interval mutation operator were adopted in the genetic processes to improve the proposed algorithm's global search capacity and prevent the algorithm from falling into local optima. The efficacy of the proposed algorithm was rigorously tested through simulations in polygonal areas with multiple obstacles. Results showed that, compared to the sequential path coverage algorithm and the genetic algorithm, the proposed algorithm reduced the length of the coverage path by 7.80%, significantly enhancing the coverage efficiency of TRUAV in the given task areas.

An improved YOLOv7-tiny detection algorithm was proposed to address the problems such as various types of surface defects in aluminum profiles, large differences in defect scales and missed detection of small target defects. The spatial pyramid pooling module was reconstructed by utilizing the residual structure, parameter-free attention mechanism (SimAM), activation function (FReLU) and clipping convolution to capture more detailed information and strengthen the multi-scale learning ability of the network. The optimized detection layer was used to obtain more small target features and location information, and improve the detection ability of network multi-scale defect. Partial convolution was introduced to replace the 3×3 convolution in the efficient layer aggregation network (ELAN), then the lightweight model was used to reduce the computing and training burden. Combined with the similarity of normalized Wasserstein distance (NWD) loss measurement, the network convergence was accelerated and the detection ability of small target defects was improved. Test was conducted on the Tianchi aluminium profile dataset, and the results showed that the improved YOLOv7-tiny algorithm achieved the accuracy, recall, mean average accuracy (mAP@0.5) and detection speed of 95.0%, 91.8%, 94.5% and 45 frames per second, respectively, when the confidence threshold was 0.25. Compared with the original algorithm, the mAP@0.5 of the improved algorithm was increased by 4.2 percentage point as a whole, the average accuracy (AP) of the dirty spot defect was increased by 13.1 percentage point; the detection results of the improved algorithm for low-resolution images and interfered images was better than of the original algorithm, which showed that the proposed method had better generalization and anti-interference ability.

A cross-domain recommendation model that utilizes source domain data augmentation and multi-interest refinement transfer was proposed in order to address the issues of difficulty in modeling interest preferences in cross-domain recommendation tasks caused by the lack of user interaction data in the source domain, as well as the problem of ignored associations between multiple interests. A source-domain data augmentation strategy was introduced, generating a denoised auxiliary sequence for each user in the source domain. Then the sparsity of user interaction data in the source domain was alleviated, and enriched user interest preferences were obtained. The interest extraction and multi-interest refinement transfer were implemented by utilizing the dual sequence multi-interest extraction module and the multi-interest refinement transfer module. Three publicly cross-domain recommendation evaluation tasks were conducted. The proposed model achieved the best performance compared with the best baseline, reducing the average MAE by 22.86% and the average RMSE by 19.65%, which verified the effectiveness of the method.

The multi-brain attention mechanism and capsule fusion module based on CapsNet (MBA-CF-cCapsNet) was proposed in order to improve the accuracy of emotion recognition. EEG-fNIRS signals were evoked by emotional video clips to construct TYUT3.0 dataset, and the features of EEG and fNIRS were extracted and mapped to the matrix. The features of EEG and fNIRS were fused by the multi-brain region attention mechanism, and different weights were given to the features of different brain regions in order to extract higher quality primary capsules. The capsule fusion module was used to reduce the number of capsules entering the dynamic routing mechanism and reduce the running time of the model. The MBA-CF-cCapsNet model was used to conduct experiment on the TYUT3.0 dataset. The accuracy of emotion recognition combined with the two signals increased by 1.53% and 14.35% compared with the results of single-modal EEG and fNIRS. The average recognition rate of the MBA-CF-cCapsNet model increased by 4.98% compared with the original CapsNet model, and was improved by 1%-5% compared with the current commonly used CapsNet emotion recognition model.

A multi-agent parking simulation framework was constructed in order to formulate autonomous vehicle (AV) parking demand management strategies. Two charging strategies for empty-load driving were proposed: a static charge based on driving distance and a dynamic charge based on road congestion levels. Rate calculation method was analyzed. Cost functions for parking lots, residential parking, and continuous empty cruising were established under these charging policies. A logit model was used to describe the choice behavior under different parking modes. The simulation of urban mobility (SUMO) was used to conduct a large-scale road network simulation experiment in Nanning’s main urban area. AV parking behavior and road network operation under both strategies were analyzed. The simulation results showed that the empty-load driving mileage of AVs decreased by 20.16% and 10.85% under the static and dynamic charging strategies, respectively. Total vehicle delay decreased by 39.80% and 43.52%, respectively. The dynamic charging strategy was adjustable in real-time based on road conditions, and operational efficiency of the road network was significantly enhanced.

Aiming at the problem of heavy load, large hysteresis and large friction disturbance of segment assembly machine, the precise control of hydraulic translation systems under friction disturbances was addressed through accurate model identification and the implementation of the iPIDD² algorithm, to improve the accuracy and efficiency of automatic segment assembly. Initially, a signal preprocessing method combining multiple algorithms for noise reduction was proposed based on the theoretical model to preprocess the output signal. Subsequently, a deviation-compensating recursive least squares identification algorithm with a forgetting factor was adopted to obtain a more accurate hydraulic system model. To achieve precise control of the translational motion of the assembly machine under friction disturbances, the iPIDD² control algorithm was proposed to achieve precise control of the translation cylinder. The research results were validated through AMESim-Simulink co-simulation and the construction of an electro-hydraulic servo system experimental platform with real-time control systems. Full-scale experimental verification was conducted under different load conditions. Results showed that compared with PID, this method had better precise control performance and smaller hysteresis time under parameter uncertainty and friction disturbance. The displacement tracking of this method was stable. The state error was less than 3 mm, which was 77.6% smaller than the maximum tracking error of PID control, and the hysteresis time was reduced by more than 10 s. This method held significant potential for improving the assembly precision and efficiency of automatic shield segment assembly under friction disturbances.

A model based on improved YOLOv5m was proposed for wolfberry pest detection in a complex environment. The next generation vision transformer (Next-ViT) was used as the backbone network to improve the feature extraction ability of the model, and the key target features were given more attention by the model. An adaptive fusion context enhancement module was added to the neck to enhance the model’s ability to understand and process contextual information, and the precision of the model for the small object (aphids) detection was improved. The C3 module in the neck network was replaced by using the C3_Faster module to reduce the model footprint and further improve the model precision. Experimental results showed that the proposed model achieved a precision of 97.0% and a recall of 92.1%. The mean average precision (mAP50) was 94.7%, which was 1.9 percentage points higher than that of the YOLOv5m, and the average precision of aphid detection was improved by 9.4 percentage points. The mAP50 of different models were compared and the proposed was 1.6, 1.6, 2.8, 3.5, and 1.0 percentage points higher than the mainstream models YOLOv7, YOLOX, DETR, EfficientDet-D1, and Cascade R-CNN, respectively. The proposed model improves the detection performance while maintaining a reasonable model footprint.

A cloud removal network for remote sensing imagery that integrated synthetic aperture radar (SAR) and optical data was proposed to address the issues of unstable performance and uneven color tones in existing deep learning-based cloud removal methods. The true texture information from SAR images and the spatial-spectral feature information from optical images were used to construct feature reconstruction tasks both globally and locally, and these tasks guided the network to rebuild missing information in cloud-covered areas. The dual-activation gated convolutional blocks and the channel attention blocks were utilized to build a spatial-spectral feature inference and reconstruction block which significantly enhanced the network’s ability to extract features from useful information in non-cloud areas. The SEN12MS-CR-TS dataset was divided into four subsets based on different cloud morphologies and cloud contents for training and testing. The experimental results showed that the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of the proposed method were 1.038 4 dB and 0.091 5, respectively, which were higher than those of the best cloud removal methods. Thus the remote sensing image thick cloud removal network, which integrates SAR and optical data, can effectively remove clouds from images and reconstruct the details beneath the clouds.

A path planning method integrating B-spline technique and genetic algorithm was proposed, aiming at the path planning problem of robots in complex obstacle environments. Firstly, a strategy based on the multi-objective A^* algorithm for generating path-type value points as well as inversing the control points was designed to generate a high-quality initial population, so as to increase the population diversity and improve the early convergence speed of the algorithm. Secondly, a novel fitness function was designed by integrating the continuity, safety and shortest of path, and the fitness value of each path was calculated. Then, the adaptive strategy was introduced to adjust the crossover and mutation operators to increase the diversity of individuals and avoid premature convergence to local optimal solutions. Finally, simulation experiments of the proposed algorithm were conducted based on MATLAB. The experimental results in complex static environment showed that the length of the robot traveling path generated by the proposed algorithm was reduced by an average of 8.22% and 2.15%, and the prematurity was reduced by an average of 88.31% and 77.08%, compared with the paths generated by GABE and IPSO-SP methods. And the paths had a second-order continuum derivability (i.e., C² continuum), which improved the robot’s traveling stability. Simultaneously, the proposed algorithm was verified to be able to complete the path planning efficiently in real environments through navigation experiments by combining with the robot operation platform.

A classification method based on convolutional block attention module (CBAM) and Inception-V4 convolutional neural network was proposed to improve the classification accuracy of group EEG signals of imagined speech. CBAM was used to emphasize significant localized areas and extract distinctive features from the output feature map of convolutional neural network (CNN), so as to improve the classification performance of group EEG signals of imagined speech. The group EEG signals of imagined speech were converted into time-frequency images by short-time Fourier transform, then the images were used to train the Inception-V4 network incorporating with CBAM. Experiments on an open-accessed dataset showed that the proposed method achieved an accuracy of 52.2% in classifying six types of short words, which was 4.1 percentage points higher than that with Inception-V4 and was 5.9 percentage points higher than that with VGG-16. Furthermore, the training time can be reduced greatly with transfer learning.

A digital twin system for the mobile operation of legged robots was proposed, encompassing the design of architecture, module structure, hardware framework, and software framework. The system enabled reliable and accurate acquisition of environmental states and robot states in mobile scenarios by integrating multiple sensor inputs and data sources. The point and line feature matching theory was used to optimize the autonomous positioning accuracy and the robustness of the legged robot, and the odometer functionality and the real-time mobile mapping were effectively achieved through integration with the environmental modeling data. A general modeling method was introduced to establish a digital twin model that ensured high consistency between the simulated robot motion state and the real robot motion state through error compensation techniques. Experimental results on both datasets and real robots demonstrated that the proposed digital twin system not only operated stably and efficiently across various legged robot platforms but also ensured the real-time state feedback and the odometer positioning accuracy. Compared with ORB-SLAM3, the memory overhead was reduced by about 68.7%, and the CPU usage was reduced by about 17.8%. The hardware experiments showed that the communication delay was basically consistent with the network delay of about 30 ms, which helped to improve the efficiency of task execution.

A multi-lane cellular automata model was established to study the influence of long-distance interweaving zones on traffic flow in urban expressways. Considering the lane-changing behavior and the intensity of lane-changing needs of vehicles at different positions within the long-distance weaving section, three distinct lane-changing rules were introduced and the long-distance weaving area was segmented accordingly. Cellular models under different traffic management strategies were constructed, considering factors such as dynamic safety distances and traffic flow management. Simulation revealed that mandatory lane-changing behavior within long-distance weaving sections easily led to localized congestion, forming bottlenecks at entrances and exits. Although the double dashed-line strategy provided more opportunities for lane-changing vehicles to exit, this advantage gradually diminished with an increasing occupancy rate. In comparison, the dashed-solid line strategy appeared more reasonable. The dashed-solid line strategy with a main road priority, while maintaining the right of way for vehicles exiting from the main road, inevitably sacrificed some efficiency in the movement of vehicles on the secondary road. However, considering the intermittent traffic flow characteristics of the secondary road, the solid-dashed line strategy 1 (the main road exits first, then followed by the secondary road) still held certain practical value.

A multi-human-robot collaboration task allocation framework considering both caregiver’s fatigue and elderly satisfaction was proposed in order to balance the subjective feelings of caregivers and elderly people. A mathematical model of caregiver’s fatigue was established by considering factors such as caregiver’s rest duration before task execution, the rapport between caregivers and elderly people, and task difficulty. A multi-objective optimization model for multi-human-robot collaboration task allocation was developed combined with elderly satisfaction. A two-dimensional double-constraint encoding method and its reasonable initialization and updating methods were proposed based on the characteristics of common tasks in elderly care scenarios. A multi-objective evolutionary algorithm was employed to solve the multi-objective optimization model by using this encoding. The final task execution plan was determined from the Pareto optimal solution set according to the min-max and max-min principles in order to prevent situations where individual caregivers experience extreme fatigue or individual elderly people have extremely low satisfaction. The simulation results demonstrate that the multi-task allocation framework for ‘multiple caregivers and multiple robots’ collaboration can achieve task allocation within a multi-caregiver and multi-robot team in the proposed elderly care scenario while balancing caregiver’s fatigue and elderly satisfaction, as well as maintaining a balance between the overall and individual caregivers, and between the overall and individual elderly people.