Reviewerly/exHarmony huggingface dataset:
Analysis of Measurement Accuracy for Stereovision Systems with Different Configurations

The accuracy of visual measurement is different for stereovision systems with different configurations since the perspective characteristics of camera. In this paper, two typical stereovision configurations including the parallel configuration and the orthogonal configuration are selected to investigate their measurement accuracies. The visual measurement models are established for these two typical configurations. Their measurement accuracies are analyzed in theory. The accuracy along the optical axis direction is much lower than the accuracy on the plane vertical to the optical axis for the stereovision system with the parallel configuration. The accuracies in any directions are at the same level for the stereovision system with the orthogonal configuration. The results of measurement experiments verify the conclusion of theoretical analysis.
Hierarchical Extraction, Planning and Behavior Tree Process Control Based on Historical Trajectory

In recent years, the research of task planning has made great progress. The use of hierarchical information to reduce the planning scope and time is one of the main ways of planning implementation. However, at present, the layered information of planners such as HTN generally needs to be set manually, so how to obtain the layered information automatically and efficiently is a big challenge. In order to deal with these challenges, this paper designs a hierarchical extraction algorithm based on PDDL description and historical planning trajectory, and innovatively deals with the fixed order problem between the operators at the same level through causal inference, so as to avoid the planning failure caused by the contradictions of them. In addition, a hierarchical programming algorithm for PDDL is designed based on hierarchical forward programming, which guarantees the completeness of solutions compared with reverse programming. Finally, the algorithm uses the behavior tree to control the process of the solution of the planner, and uses another distributed multi -agent scheduling system based on the behavior tree to allocate the task execution relationship, so that the algorithm can adapt to the constraints of the start and end of the collaborative task.
Temporal Context Attention-Based Tracker for UAV Tracking

Inspired by the framework of TCTrack, a temporal context attention-based tracker is proposed for Unmanned Aerial Vehicle(UAV). The instruction of attention mechanism enables trackers to pay attention to more effective feature knowledge in order to compensate the problem that shallow network feature extraction of UAV tracker is limited, therefore the attention module of multi-feature information fusion based on temporal context is proposed to produce more distinguishable feature representation. In addition, considering the relationship between context similarities, we propose the lightweight temporal transformer(LT-Trans) to increase the execution speed of the model. Finally, the evaluation experiments of four air tracking benchmarks show our tracker compared with currently popular trackers makes more excellent performance.
Rényi Differential Private Proximal ADMM for Distributed Machine Learning

In this paper, we mainly focus on the privacy protection of distributed optimization algorithms, which means some agents cooperates to solve a problem without leakage of private data. Based on the alternating direction multiplier method (ADMM) and in the case that the objective function contains non-smooth terms, we proposed a proximal ADMM-based algorithm. Then, we use Gaussian noise to disturb the primal estimates to ensure the privacy. In addition, the Gaussian noise decays linearly which ensures the exact convergence of the proposed algorithm, and we show that the algorithm is Rényi differential privacy. Moreover, we establish the convergence of the provided privacy preserving algorithm under some general conditions. Finally, a numerical example on a real dataset is conducted to show the performance of the proposed algorithm.
Simulated Ising Annealing Algorithm with Gaussian Augmented Hamiltonian Monte Carlo

A numerical technique called Simulated Ising Annealing (SIA) uses digital computers to obtain approximations of the ground states of Ising models. The quadratic unconstrained binary optimization (QUBO) problem in combinatorial optimization, which is challenging to solve through exhaustive search, has significant linkages to the Ising model. Therefore, SIA's main objective is to identify a better solution in finite iterations. In order to enhance the performance of the simulated annealing procedure, we adopt a Gaussian augmented Hamiltonian Monte Carlo (GAHMC) method in this study. We contrasted our algorithm with Gibbs sampling and local quantum annealing method. Finally, we discuss its application on QUBO problem.
An Intelligent Garbage Sorting Robot System Based on Machine Vision and Knowledge Base

Garbage disposal has always been a widely concerned environmental problem. At present, garbage sorting mainly depends on manual work, with low efficiency, poor accuracy and easy to harm the staff. One way to solve the problem is to develop robots with garbage sorting function instead of manual operation. This paper presents a design scheme of intelligent garbage sorting robot system based on machine vision and knowledge base. The coarse localization of garbage is firstly detected by the YOLO_v5 algorithm with the RGB camera and the bounding box of the garbage can be obtained in the image coordinate system. Then, the depth information of the garbage is retrieved by the depth camera, and the garbage coordinates are obtained. After the coordinate conversion, the position of the garbage is set as the robot navigation target and the trajectory planning is carried out. When the robot reaches the target position, the YOLO algorithm is then utilized to refine the classification and the position of the garbage and the robot arm equipped on the robot is utilized to pick up the garbage. After the garbage recognition, the garbage type and the corresponding garbage bin location information can be retrieved according to the knowledge base. At the same time, it stores the garbage information of the current position in the knowledge base to facilitate the subsequent optimization of the cruise track. Finally, the robot navigates to the corresponding bin location, puts down the garbage, and completes the garbage sorting task. Experimental results show that this method can accurately identify garbage and effectively improve the efficiency of garbage sorting.
A Gradient RBF Network Based on Multi-Innovation Algorithm for Nonstationary Time Series Prediction

Time series of natural phenomena including human behaviours usually show irregular fluctuations. In this paper, a fast tunable gradient radial basis function (GRBF) network based on the multi-innovation algorithm for modeling and predicting nonstationary and nonlinear time series is proposed. In the process of constructing the frame of the network, the orthogonal least squares algorithm is used to simplify the frame of the GRBF network. During the online adaption, the node with the minimal effection is replaced with an appropriate node while the network cannot track the changes of the time series. The algorithm based on the multi-innovation theory is used to refresh the weight vector. Lorenz time series is used to verify the online prediction performance of the proposed method.
A Modified Deeplabv3+ Model Based on Polarized Self-Attention Mechanism

In this paper, we propose a modified Deeplabv3+ model based on the combination optimization of atrous convolutions and Polarized Self-Attention module, aiming to enhance the semantic segmentation performance of small-scale objects in various scenarios. The improvements of this paper mainly focus on the following two aspects. Firstly, we optimize the combination of atrous convolutions in the Deeplabv3+ model by using lower atrous rates, so that it can expand the receptive field while enhancing the recognition ability of small-scale targets. Secondly, by introducing the Polarized Self-Attention module, the semantic segmentation of small objects is further optimized, while capturing more global semantic information to ensure overall semantic segmentation. Numerous experimental results demonstrate that, compared with the original Deeplabv3+ network model, the modified model improves the segmentation accuracy by 1.8% and 1.5% on the Cityscapes and Pascal VOC 2012 test datasets.
Set-Membership Filtering-Based Cooperative State Estimation for Multi-Agent Systems

In this article, we focus on the cooperative state estimation problem of multi-agent systems with absolute and relative measurements. The purpose of each agent is to estiamte its own state in a Set-Membership Filtering (SMFing) manner by utilizing locally available measurements and interchanging the necessary information among its neighbors. To handle this problem, we provide a centralized SMFing framework as a benchmark of distributed SMFs and propose a finite-horizon method called OIT-Inspired centralized constrained zonotopic algorithm. Moreover, we put forward a distributed SMFing framework and develop a distributed constrained zonotopic algorithm. Simulation results corroborate the effectiveness of our proposed algorithms.
A Single-Stage 3D Object Detector with Multi-scale Input and Output

3D object detection plays an important role for autonomous vehicles and a number of models have been proposed to handle this task. Among them, PointPillars has an outstanding advantage on fast encoding point clouds. However, the 3D structural information are always weaken by the voxelization. Furthermore, the single-scale output seriously limits the detection accuracy on the objects of different sizes. Motivated by that, this paper proposes a single-stage object detection network with multi-scale input and output. First, the grids of different scales are introduced to voxelize the point cloud and generate feature maps of different resolutions. Then, the maps are processed by an efficient fusion network. Finally, a multi-scale detection layer is designed to obtain 3D bounding boxes and categories. Comparative and ablative experiments demonstrate that our scheme can effectively improve the performance of 3D object detection.
Robust Weather Recognition in Images via Interactive CNN-Transformer

Weather recognition is a fundamental yet challenging task for the multi -sensor perception system of driverless vehicles. It is difficult for current joint perception framework to autonomously recognize weather scenes and adaptively adjust the weight of camera involved in perception. This paper proposes a weather recognition method with a new interactive CNN - Transformer architecture to learn global and local contextual information from images. The framework starts by deepening and widening the convolution blocks to obtain multi-scale local scene features. We re-weight different feature channels by channel-attention mechanism. Then, a new interactive feature fusion network between convolutional structure and stacked Transformer is developed, which can gradually complement the global feature information lost in CNN. We evaluated the proposed method on a public weather image dataset. The recognition accuracy reaches 95.14%, which improves 5.68% and 6.15% compared with the ResNet-50 and ViT16 and achieves state-of-the-art performance on the experimental dataset. At the same time, the comprehensive evaluation of recall and precision show that the proposed method is more robust when dealing with multi-class weather recognition tasks.
A Reinforced Particle Swarm Optimization Algorithm Based on Beetle Antenna Search for UAV Path Planning

Traditional UAV path planning algorithms are difficult to achieve UAV path planning in complex 3-dimensional environments. In this paper, a UAV 3-dimensional path planning algorithm combining particle swarm optimization algorithm and beetle antenna search algorithm is proposed. Tent map inverse learning is introduced to initialize the algorithm, and B-spline curve is used to smooth the final formed paths. Experimental results show that the improved algorithm can obtain better paths faster in complex 3-dimensional environments.
Uncalibrated Visual Servo Control Algorithm Based on TL-MobileNet-YOLO

The robot visual servo system is a nonlinear, multi-parameter and high coupling system. The controller designed based on the calibration model is not stable, and it is difficult to keep working stably for a long time. In order to get rid of the tedious coordinate system calibration steps, researchers proposed uncalibrated visual servo control. In the traditional uncalibrated visual servo method, the image Jacobian matrix is usually used to approximate the nonlinear relationship between the manipulator and the image features in a small range. However, this method has some limitations in space, and visual features need to be manually set and are easily affected by the environment. In order to solve the problems of the traditional algorithms, a visual follow-up algorithm based on MobileNet-YOLO is proposed in this paper. In this paper, the MobileNet feature extraction network is used to replace the feature extraction network of YOLO, and the network structure is improved. Then, in order to fit the hand-eye mapping relationship between the manipulator and the camera, the network output layer is constructed after the MobileNet-YOLO network to predict the control law of the manipulator. Finally, the experimental verification is carried out on the experimental platform of the WPR1 manipulator.
Design of a Collision Mitigation Quadrotor UAV with Compact Spherical Configuration

In order to endow quadrotor unmanned aerial vehicles (UAVs) with collision safety capabilities, a compact spherical collision mitigation quadrotor UAV (SCMQ UAV) design scheme is proposed. The scheme consists of a spherical protective shell to reduce the damage to the aircraft structure and an anti-disturbance three-axis internal framework to mitigate the disturbance to its attitude during collisions. Particularly, the inner framework employs a curved layout and suspended design, resulting in a more compact propeller layout and a reduced wheelbase for the UAV. As a result, a smaller protective shell can be used. The study also analyzed the potential interference on attitude caused by the pendulum structure of the SCMQ UAV during collisions and its relationship with collision velocity. Finally, experimental comparisons verifies the collision safety capabilities of the proposed design.
Trajectory Planning Based on MINVO Basis for Autonomous Vehicles in Lane Change Scenarios

This paper focuses on the problem of collision-free lane change for autonomous vehicles under dynamic obstacle road scenarios and proposes a new trajectory planning algorithm for autonomous vehicles. With the goals of dynamics feasibility, comfort, and safety in mind, we develop a B-spline-based trajectory planning method capable of generating trajectories which satisfy all the desired objectives. Specifically, (i) the maximum velocity, the maximum acceleration and the maximum curvature of the trajectory are taken into account explicitly to ensure the comfort of the ride and the traceability of the planned path; (ii) MINVO basis is used to obtain outer polygon representations with minimum area of each interval of the host vehicles' trajectories. These polygon representations are separated by designed artificial hyperplanes to ensure collision avoidance; and (iii) the objective function is designed to guarantee both feasibility and comfort with the given trajectory by incorporating a jerk term and a deviation from the goal point for the penalty. Finally, simulation experiments on both the ample and aggressive scenarios are conducted to verify the effectiveness of this proposed trajectory planning algorithm.
Point-Voxel Fusion for 3D Object Detection

In 3D object detection, network prediction accuracy is greatly affected by point cloud's feature richness. However, the feature richness depends on fine-grained features extracted by the network. Currently some methods use voxel encoding approach continuously down-scaled by 3D convolution to improve the detection efficiency, but lose too many fine-grained features. Some methods directly inputting the original point cloud into the Multi-layer Perceptron (MLP) for feature extraction, which can retain more fine-grained features, but greatly reduce the detection efficiency. This work combines voxel features and point features to obtain a fused 3D map. We use an attention mechanism module that combines semantic features with spatial features to progress the former 3D feature map, which is used to constitute a richer 3D feature structure to reduce the loss of Z-axis features. Since the object geometry structure information is important for the detection task, we design a geometry-oriented auxiliary network that is jointly optimized by supervising two tasks in the training phase to guide the backbone network to understand the target structure features and discard them in the inference phase. The experiments show that our proposed detection method outperforms some previous methods in KITTI 3D/BEV detection.
Multi-adversarial Testing and Retraining for Improving the Robustness of Autonomous Driving Policies

Autonomous vehicles, especially those based on deep reinforcement learning, are known for their susceptibility to the adversarial perturbations. To enhance their robustness, it is imperative to not only detect their decision errors through testing, but also fortify their robustness against these errors. This paper proposes an iterative optimization method that trains multiple adversarial agents with varying adversarial intensities to identify decision errors in the driving vehicle and enhance its robustness by retraining to counteract these adversarial agents. The effectiveness of the method is evaluated in a lane-changing scenario and the results demonstrate improved robustness of deep reinforcement learning based autonomous driving strategies compared to the adversarial reinforcement learning with a single adversary.
Robust Kalman Filtering Under Correlated Noise, Uncertain Variance Multiplicative Noise and Multiple Networked-Induced Uncertainties

By using the augmented method with fictitious noise and derandomization approach, the original system with uncertain noise variances, multiplicative noises, missing measurements, packets dropouts and one-step random measurement delays is converted into one only with uncertain fictitious noise variances. Then, based on the minimax robust filtering principle, the steady-state robust Kalman filter is designed for worst-case system with conversative upper bound of fictitious noise variances. The actual Kalman filtering error variance is guaranteed to have minimal upper bound for all admissible uncertainties. The robustness is proved by extended Lypunov equation method and matrix decomposition method. A simulation example used to tracking system shows that the correctness and effectiveness of the proposed results.
LPHD: Line Perspective Hexagonal Screw Detection

In industrial production, hexagonal screws are widely used as fasteners, and it is crucial to have fast and accurate inspection of these screws for automated screwing processes. However, this remains a challenge due to various factors such as environmental distractions, lighting, shadows, and screw positioning. To address this, the Line Perspective Hexagonal Screw Detection (LPHD) algorithm has been proposed in this paper, which introduces four innovative techniques: canny value, hexagonal base, perspective correction, and double boundary. The LPHD algorithm starts by feeding the image captured by the robot camera into the YOLO model to obtain the bounding box of the hexagonal screw. Then, the result is subjected to canny edge detection and LSD/FLD line detection. Since the head of a hexagonal screw is a regular hexagonal prism, only three edges are needed to determine a hexagonal contour, which is referred to as the hexagonal base. Perspective correction is proposed to eliminate the camera's perspective effect. If multiple contours are found, a combination of canny value and double boundary is used to identify the optimal result. Ultimately, the LPHD algorithm is applied to find the hexagonal contour in this box. This paper also compares the efficiency and accuracy of LPHD with Mask R-CNN. The results show that LPHD is nearly three times more efficient than the Mask R-CNN algorithm with approximately the same accuracy. Therefore, the LPHD algorithm is a promising solution for detecting hexagonal screws in industrial settings.
Enhanced Generalization of Variational Quantum Learning Under Reduced-Domain Initialization

Trainability and generalization are two key issues in quantum machine learning on the basis of variational algorithms. It has been demonstrated in [1] that with the reduced-domain initialization strategy, the trainability of variational parameters can be greatly enhanced for complex objectives and general deep circuits. In this paper, we further explore the generalization of variational quantum learning under the reduced-domain initialization. We theoretically derive the upper bound of the generalization error and demonstrate the enhancement as compared to that under the usual parameter initialization methods. Thus, our results clearly show that the reduced-domain initialization can push the boundary of applicability of deep variational quantum learning toward practical applications.
UAV Path Replanning Based on BeiDou Short Message Communication Function

In this paper, we study the unmanned aerial vehicle (UAV) path replanning problem based on BeiDou short messages which can provide reliable position information in environments with weak Global Navigation Satellite System (GNSS) signals. First, we analyze the error characteristics of the inertial navigation system (INS) with the BeiDou short message communication function as its auxiliary source, and establish the UAV position uncertainty model. Then, the path replanning problem for the UAV with the BeiDou short message communication function is rigorously formulated, which takes the path length, path safety, energy consumption, and position uncertainty into account. To solve this path replanning problem, we propose a particle swarm optimization-based improved interfered fluid dynamical system (PSO-IIFDS) algorithm. Simulation results show that the proposed replanning method can significantly improve the task success rate, which corroborates the effectiveness of our design.
Short-term Photovoltaic Power Forecasting Based on Similar Day Selection and Deep Neural Network

Photovoltaic(PV) forecast plays an important role in power dispatching, short-term forecast methods mainly use numerical weather prediction(NWP) data and build models to predict. In this paper, a day-ahead PV power forecasting method is proposed, this method is based on similar day selection algorithm and long short-term memory neural network(LSTM). In the process, the daily feature matrix is obtained by correlation analysis and principal component analysis(PCA) of NWP data. Similar day selection algorithm is based on the matrix norm of the daily feature matrix. In the training stage, wavelet transform is used to reconstruct all PV power sequence to achieve the purpose of noise reduction, and four LSTM submodels are established for predicting according to the seasonal weather condition. Finally, historical power output of similar day and other relevant parameters are input into models for training. The experimental results show that the method in this paper has a high accuracy.