Reviewerly/exHarmony huggingface dataset:
Manipulator Motion Planning Based on Prior Knowledge Guidance in Complex Constrained Environment

Manipulator motion planning in the complex constrained space is essential for realizing the universal service robot. However, the limited space makes it challenging for the manipulator to quickly plan a feasible path and easily fall into a local minimum. Moreover, the feasible path is tortuous and unsmooth due to complex space constraints. Therefore, an improved RRT*-Connect algorithm is proposed, which uses prior knowledge to guide the sampling process of two trees, aiming at fast convergence to a feasible path in various scenes. Then, constraints such as path length and joint rotation angle are introduced in this paper. Based on the above constraints, trajectory optimization is carried out to obtain the optimal path. The experimental results show that the non-uniform sampling method can quickly find a feasible path. After trajectory optimization, a smooth, efficient, and stable path is obtained. When the constrained space complexity reaches 95.56%, our proposed algorithm can guarantee a significantly high planning success rate.
Fast Path Planning Based on Bi-Directional RRT<sup>*</sup> for Mobile Robot in Complex Maze Environments

The Rapidly-exploring Random Tree (RRT) algorithm and various improved algorithms are being used for path planning in many different environments. Because of the probabilistic completeness of the RRT algorithm, if there is a feasible path, the RRT algorithm should be able to find it. However, in maze environments, the cost of computation and computation time will increase. There, in this paper, a new algorithm Exploration and Exploitation RRT (EE-RRT) based on Bi-directional RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> (B-RRT*) is proposed for path planning in maze environments. During the initial stage of path planning, two trees with starting point and ending point as root nodes grow at the same time, exploring the global environment. Once all feasible areas are discovered, the algorithm stops searching and begins to exploit the feasible areas to find better paths. The simulation results verify the effectiveness and superiority and of our proposed algorithm which has the fastest speed of path planning and shorter path length than RRT*-connect and Bi-directional RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithms.
Optimal Task Assignment and Path Planning for Multiple Patrol Robots in Makeshift Hospitals

Patrol robots play important roles in security guarantees for makeshift hospitals that are established to assist emergency medical treatment such as pandemics and natural disasters. To improve the effectiveness of the robot patrol and save the cost, the key is to develop an optimal task assignment method and then design a costless motion path for robots. In this paper, the task assignment and path planning for multiple patrol robots are formulated as a multi-constraint multi-objective optimization problem, and a new optimization algorithm based on the particle swarm algorithm is proposed. Due to the irregular shape of the makeshift hospital, the corner collision protection rules are designed to ensure the collision avoidance. In addition, the path planning costs of different task assignments are calculated. Our study demonstrates the effectiveness of the proposed algorithm for improving security in makeshift hospitals while optimizing resource allocation.
Complex Network-Based Genetic Algorithm for Optimization

Genetic algorithm (GA) is a classical swarm intelligence algorithm, where selection, crossover and mutation are operated on a population to simulate the evolution of genes. However, it faces the limitations of premature convergence and falling into local optimal solutions. To overcome this problem, a complex network-based genetic algorithm (CNGA) is proposed by thoroughly analyzing the characteristics of complex network and exploiting the similarity between complex network and swarm intelligence. Firstly, to reduce the possibility of falling into local optimal solutions, the complex network structure is designed for decentralized local search. Secondly, to improve the accuracy and convergence speed, the evolutionary game strategies are proposed for real-time update. To test the feasibility and superiority of the proposed CNGA, six benchmark functions and five other swarm intelligence algorithms have been utilized. The results show that the CNGA has superior performance in terms of accuracy, stability and convergence speed. This provides a new idea for the development of swarm intelligence algorithms.
An Event-Triggered Unscented Kalman Filter with Correlated Noises and Random Packet Dropouts

Considering the effects of correlated noises and random packet dropouts on filter performance, the design of an event-triggered filter for a class of nonlinear networked control systems with correlated noises and random packet dropouts is studied in this paper. Under the assumptions that the process noise and measurement noise of the system are cross-correlated in one-step and the measurement values are randomly lost during transmission, an event-triggered filter based on unscented Kalman filter is proposed and implemented by introducing noise correlation coefficients into the filter, compensating for the lost measurement values, and designing an event-triggered mechanism whose threshold value can change with the filtering process. Finally, the designed event-triggered filter is verified to maintain good estimation performance while reducing the number of measurement transmissions through simulation.
Helmet-Wearing Detection Algorithm Based on Improved YOLOv5s

In high-risk industries such as safety production and aerial work, the use of helmets by workers constitutes a crucial measure for preventing injuries. Automated detection of helmet-wearing can enhance objectivity and reduce labor costs. However, the current object detection algorithms for helmet-wearing may overlook small-sized targets, leading to missed detections. To address this issue, this paper proposes an improved algorithm for helmet-wearing detection based on YOLOv5s. First, the attention mechanism is introduced into the multi-scale fusion module of YOLOv5s to enhance the network's expression ability for small target features. Additionally, the Path Aggregation Network (PANet) is replaced by the Weighted Bidirectional Feature Pyramid Network (BiFPN) to improve the network's computational capability and fuse more feature information. Finally, the GIoU Loss function in the algorithm is replaced by the CIoU Loss to make the network converge faster and obtain higher accuracy in regression localization during training. The experiment results show that the mAP and FPS of the improved algorithm have increased by 2.6% and 2.7 frame/s. The improved algorithm can meet the requirements of accuracy and real-time performance for helmet-wearing detection in practical application environments.
A Hierarchical Pooling Method Using Structural Entropy and Information Bottleneck

The problem of graph classification in graph neural networks has received extensive research. In the graph classification tasks, graph pooling is one of the important research directions, which aims to reduce the dimensionality of the representation and obtain the prediction label for the entire graph. However, due to fixed compression quotas and node discarding strategies, the commonly adopted hierarchical pooling methods often suffer the graph structure destruction and information loss. And their model training will be time-consuming. To address these issues, we suggest a hierarchical pooling method using structural entropy and information bottleneck (HSEIBPool). In this work, a global optimization algorithm is proposed to convert graph data samples to coding trees and generate cluster assignment matrices in one step. Then, we introduce information bottleneck principle to optimize the expressiveness and robustness of the model, which is applied to process the pooling layer representations. Our model consistently outperforms previous models on various datasets, as demonstrated by the results of our experiments.
Online Moving Object Detection from Aerial Videos by Leveraging Geometric Constraints

The fast growing of consumer drones has prompted a lot of relevant applications such like moving object detection. Different methods have been proposed to detect moving targets in aerial videos, many rely on dense optical flow and others are based on frame differencing. They are either very time consuming or susceptible to illumination changes. In this paper, two geometric constraints are leveraged to detect moving targets for low-attitude aerial platforms. First is the homography constraint which assumes that every two consecutive frames are well related by a 3 × 3 non-singular matrix. Second is the epipolar constraint that any pixel in one image is projected onto a line in another image. Since geometric constraints widely exist in aerial videos, the proposed method is more resistant to challenges like illumination variation and background clutters. Besides, compared to multi-frame based methods, the proposed algorithm only takes a pair of consecutive frames as input, making it more efficient and easier to process fast moving targets. Extensive experiments on public data show that the proposed method not only has real-time efficiency on a single CPU, but also brings with a low false alarm rate when dealing with challenging scenarios.
Active Informative Receding Horizon Planning for Quadrotors

Motion planning for quadrotors is still a challenging problem due to the uncertainty and complexity of the environment. In this context, we propose an active informative receding horizon trajectory generation method that incorporates the informative path planning and B-spline-based trajectory optimization to achieve a smooth, safe, and efficient trajectory for UAVs. The proposed method includes an Informative A* (IA*) path planning algorithm, which not only considers the path length but also takes into account the perception information. Specifically, the informative term is calculated by transforming the information matrix of the least-squares problem based on the odometry, which can reflect the environmental texture. The trajectory is then generated based on a B-spline curve, which takes into account collision avoidance, smoothness, and dynamic feasibility. Finally, comparative simulations are conducted to verify the superior performance in improving the localization accuracy.
Cooperative Path Planning Method Based on Road Network Constraints for Vehicle-Mounted Multi-Rotor UAV Swarm

In order to solve the problems of multi-task point access in urban scenarios with constraints of the actual road network and diverse task duration, a cooperative path planning method based on ant colony algorithm (ACO) is proposed for vehicle-mounted multi-rotor UAV (unmanned aerial vehicle) swarm. Firstly, considering road network constraints, UAV performance parameters, task time and other constraints, the adjacency relationship between each task point and the road network is calculated in order to solve the optimal path of the UGV (unmanned ground vehicle). Then, the feasible path of the UGV is discretized in order to calculate the stopping point of the UGV and the task points sequence that can be accessed by each stopping point. Finally, the path planning of multi-UAV swarm accessing multi-task points is carried out for each stopping point. The real road network data is adopted for simulation experiments, the results show that the method can effectively plan the path of vehicle-mounted multi-rotor UAV swarm, achieve the shortest total time and coverage total task points.
Maximum Correntropy AMM Filter for Linear Systems with Non-Gaussian Noise

The uncertain state estimation of the target model for systems with non-Gaussian noise is considered in this paper. The traditional filtering algorithm is only suitable for eliminating Gaussian white noise and cannot be applied to the complex non-Gaussian environment effectively. In addition, the filtering problem with an uncertain motion state processed by a single model will increase the estimation error and even cause target loss. The maximum correntropy AMM (MC-AMM) filter is proposed, which can solve the problem of time-invariant model uncertainty in the case of non-Gaussian noise. Firstly, a more general target tracking method is proposed based on the autonomous multiple model method and the maximum correntropy Kalman filter. Then, the principle of MC-AMM is given in detail. Finally, the MC-AMM method is verified in the theoretical function model and the maneuvering target scene. Compared with the AMM algorithm and the Gaussian mixture AMM, the simulation results show that the MC-AMM algorithm proposed in this paper is feasible under the complex situation of non-Gaussian noise and uncertain motion model. It can improve the accuracy of target state estimation and effectively solve the problem of difficult adaptive background estimation.
Distributed Path Planning for UAVs Based on $\mathbf{A}^{\ast }$ Algorithm of Dubins ath

This paper proposes a path planning method for unmanned aerial vehicles(UAV). Aiming at the problem that the traditional grid-based <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{A}^{\ast}$</tex> search algorithm does not consider flight performance constraints, which cause the path discontinuity. This paper adopts the Dubins curve to find effective nodes, and combines the idea of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{A}^{\ast}$</tex> heuristic search to generate obstacle avoidance paths composed of Dubins curves for UAVs. This paper then combines the proposed algorithm with leader-follower control algorithm and applies the algorithm to multiple UAVs formation. The simulation and experimental results show that the method is feasible. Results shows it takes advantage over the commonly used <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{A}^{\ast}$</tex> algorithm applied on a grid map in aspects of the optimality and efficiency, and it can achieve obstacle avoidance in formation flying.
Incremental Image Classification Method Based on Proxy Features

A typical downside of deep learning methods is the problem of catastrophic forgetting, that is, once a new dataset is used to train an existing model, the model will lose the ability to identify the original dataset. In this paper, an incremental image classification method based on proxy features is proposed, which can add new categories by reasoning. Firstly, the image features are extracted by the feature extraction model, and a triplet loss function based on proxy features is constructed to improve the feature mapping ability of the feature extraction model as well as the convergence speed of the model in the training process; Secondly, a method of computing proxy features is proposed, based on which the proxy feature library is constructed, and the increment of the proxy feature library is used to achieve the increment of the image category; Finally, the performance of the proposed algorithm is compared with other similar algorithms, and the experimental results show that the proposed triplet loss function based on proxy features performs better than that of the traditional triplet loss function. In the well-established dish recognition dataset, the classification accuracy of the base class dataset is 93.9%, and the average classification accuracy of the incremental classification dataset is 89.4%, which verifies the feasibility and effectiveness of the proposed method.
Pinning Controlled Nodes Detection in Urban Traffic Networks via an Improved WVoteRank Approach

Urban traffic networks are complex systems, where intersections are interconnected by road links, and the dynamics of traffic flow is controlled by traffic lights. Macroscopic fundamental diagrams based traffic control pave the way for alleviating urban traffic congestion problems at the networked level. In order to have better control of the traffic network, the more important areas of the urban road network could be operated via pinning control after dividing it into several homogeneous sub-regions. In this paper, we identify the pinning controlled nodes of the traffic road network based on the extended VoteRank. The improved WVoteRank is obtained based on WVoteRank and VoteRank ++. The characteristics of the regional road network itself and the road segments connecting two regions are taken into account. In this way, a pinning controlled nodes identification algorithm applicable to traffic networks is formed. With the simulation experiments based on the real traffic network, several pinning controlled regions are obtained. The simulation results show that the improved WVoteRank can identify the key regions in the traffic network.
CWR: Classification Weight Regularization for the Trade-Off between DNN's Accuracy and Robustness

Although deep neural networks performing well in vision and speech processing tasks, it has been shown to be vulnerable to subtle input perturbations. Ensuring robustness of models against corruption of input data becomes an important criterion for designing reliable systems. Existing defense methods are mostly effective in improving the robustness of the model, but ignore its accuracy on clean data. In order to obtain model with better robustness and accuracy, we creatively use the label information of the original samples and adversarial examples during model training. Our method regularizes the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{2}$</tex> norm of the weight corresponding to the classification of the network, and treats it as post-processing after normal training has finished. From the experimental results, our proposed algorithm performs well in real-world and noisy datasets, enhances model robustness with a minimal decrease in the natural accuracy.
A Scalable ARM+FPGA-Based CNN Accelerator with Limited Hardware Resources

General processors cannot meet the requirements of low power consumption and high performance in mobile application scenarios, the hardware research platform of CNNs has begun to turn to high-performance computing platforms such as GPUs and FPGAs. The GPU contains a large number of stream processors to execute operations in parallel, which greatly reduces the operation time of the model. However, because of its large power consumption, mobile applications can hardly afford the GPU's power requirement. Compared with GPUs, FPGAs also have parallel architecture, but the power consumption is much lower, which is suitable for CNN mobile application scenarios. In this work, concerning that FPGAs with large resource can meet the deployment requirements of CNNs but they are too expensive. How to deploy CNNs with little-resource has become an urgent problem to be solved. Based on this, we propose a scalable ARM+FPGA-based CNN accelerator with limited hardware resources, data flow is controlled through ARM, network is computed through FPGA. LeNet is adopted as the acceleration target, experiments were carried out on MNIST datasets and under the little-resource FPGA xc7z020clg400-2. The result show that at the frequency of 100 MHz, the accuracy of the accelerator prediction of 10000 images is 98.57% and the processing time of each image is 16.09ms.
Optimization Method of Neural Networks via Fractional-Order of Gradients*

The optimization algorithm was proposed to tackle the neural network weights effectively update problem. Typically, a good optimization algorithm is used to obtain stable and efficiently weights update, and has the better generalization ability. However, the neural networks are complex, which is impossible to obtain a set of perfect network model simply. In other words, current optimization algorithms have their own limitations, especially when the structure of the neural network is more complex and the amount of data is larger. In order to improve the recognition accuracy of the neural network, we propose a novel method based on the fractional-order of gradient, and named as FracG. The proposed fractional-order gradient optimization algorithm using fractional order to rectify the gradient. We take full advantage of the wonderful role of fractional order in the traditional control field, which has unique memory characteristics, and help avoid traps in the local extrema point in the optimization of deep neural network. Our FracG can compare with existing famous optimization algorithms. At the same time, the effectiveness and performance of our algorithm are proved by experiments.
Improved A* and DWA Based Dynamic Trajectory Planning Algorithm for Multi-UAVs Formation

A dynamic trajectory planning algorithm for leader-follower Multi-UAVs formation is proposed, which is composed of the improved A* and dynamic window algorithm (DWA). It aims to address trajectory planning, obstacle avoidance and formation maintenance problems in dynamic environment. Firstly, a hybrid trajectory planning strategy which contains global and local planning is investigated. An improved A* with a novel node selection and a bi -directional path smoothing method is developed to achieve global planning for the leader. On the basis of the generated global trajectory, DWA is transformed into the linear velocity space to realize local planning for multi - UAVs formation, moreover rolling windows is introduced to cut down its computation. Then, the leader-follower based multi-UAVs formation dynamic trajectory planning problem is modeled utilizing the consensus principle. It is solved through the proposed hybrid algorithm. Finally, simulation results are presented to validate the fine performance of the designed algorithm.
Research on Path Planning of Unmanned Ship Based on DWSS Algorithm

In this paper A global planning method of Bezier-DWSS based on super-twisting algorithm and Beetle Antennae search algorithm is proposed to optimize the Grey Wolf Optimizer algorithm. The method integrates the second-order spiral advance principle in the super-twisting algorithm and the principle of using the two tentacles on the head ofthebeetle for optimal search in the Beetle Antennae search algorithm into the Grey Wolf Optimizer algorithm, so that it can avoid falling into the problem of local optimality in the search process. Then, the Bezier curve is used for the de-sharpening of the unmanned ship path, so that the unmanned ship can navigate smoothly in the real environment. Simulation results show that the algorithm has smoother paths, fewer fractional points, a larger search range and can produce better paths with more detailed local exploration. Key Words: super-twisting algorithm, Beetle Antennae search algorithm, Grey Wolf Optimizer algorithm, Grid method, Bezier curve
Multi-Graph Contrastive Learning Clustering Network

Graph contrastive learning methods often construct numerous homogenous graphs by randomly adding edge perturbance and removing edges to/from one given graph, while it is difficult to guarantee the quality of these augmented graph structures. To fully guarantee and utilize the rich structural information provided by the above heterogeneous graphs, we propose a novel multi-graph contrastive clustering network model that collaborates various types of relationships between nodes. In contrast to traditional contrastive learning methods that only regard each node as one positive sample and its neighbors as negative samples within a single graph, we further build the contrastive constraint between nodes of the same sample in different graphs, improving the node representation capability from the topology and sample attribute aspects. The designed multi-graph attention mechanism assigns more weights to significant graphs and nodes. Experimental results on three public multi-graph datasets demonstrate that the proposed method achieves satisfactory performance compared to other state-of-the-art clustering methods.