Reviewerly/exHarmony huggingface dataset:
Path Optimization of AGV in The Environment of Smart Bookstore

This article is based on the smart bookstore in the laboratory, aiming at the problem that the RRT* algorithm has a big sampling range, vast traversed nodes, low search efficiency and long time for the searched path in the path search, a target bias strategy is set up, which combines the artificial potential field method and the RRT* algorithm. And applied to the path planning of AGV to accelerate the movement of AGV to the target point, improved the efficiency of path search, and compared the RRT algorithm, RRT* algorithm, and improved RRT* algorithm through Matlab simulation experiments, the simulation results illustrate that the improved RRT* algorithm used in this article can reduce the number of traversing nodes and take a shorter time to find a reasonable driving path in the environment of the smart bookstore.
Towards Adaptive Behavior Trees for Robot Task Planning

Behavior Tree, known as modular and reactive, is widely used in industry and research. Its variant, the Reactive Behavior Tree (RBT), can effectively resolve the unpredictable environments through dynamic expansion based on actions and predicates. However, in partially observable environments, considering the observe limitation of scene, the existing RBT expanding method will break the necessary sequential dependence between the predicates and cause the agent to fail to perceive state changes in the environment, leaving the planner in a spin state. We proposed an optimized RBT method, where the RBT can repair the sequential dependence by rollbacking dynamically. We verified the optimized method in a simulation environment, and our method was shown to improve the effectiveness and robustness of RBT in partially observable environments.
A Dual-Channel Network Based GAN for Low-Dose CT Image Denoising

Denoising of low-dose computed tomography (LDCT)images is a hot issue in the field of medical images. In this paper, a dual-channel denoising network based on GAN architecture is proposed to solve the key problems of image blur and loss of detail structure in the process of LDCT denoising. In the generator part, a content learning channel and a noise learning channel are used to learn images at the same time. In the content learning channel, an improved U-Net network is used to learn the image content to generate a normal dose computed tomography (NDCT) image. In the noise learning channel, firstly, wavelet transform is used to obtain high-frequency components, and then the improved DnCNN network is used to learn the noise. Finally, the NDCT is obtained. Through the fusion layer, the results of the two are fused to obtain a better image. Through the ablation implementation and compared with the conventional methods, the method suggested in this research has a better denoising effect and a certain suppression effect on the blurring of detail structures.
Accurate 3D Single Object Tracker in Point Clouds with Transformer

3D single object tracking (SOT) in point clouds is a fundamental task in autonomous driving and robotics. Motivated by the success of transformer trackers in 2D tracking, we develop a 3D tracker in point clouds with transformer named Trans3DT. Trans3DT consists of three main designs: 1) Different from most previous trackers that use PointNet++ as the backbone, we propose a transformer feature extraction network to efficiently weigh point features to focus on deeper target cues. 2) Instead of using cosine similarity, we propose a novel target feature embedding network that fuses template and search area features in a global manner. 3) A voxel-to-BEV target location network is applied with a lightweight channel-related convolution block (CRCB) to enhance the BEV features. Extensive experiments demonstrate that Trans3DT achieves a new state-of-the-art performance on the challenging KITTI and nuScenes tracking benchmarks.
Base-Arm Collaborative Planning of A Dual-Arm Mobile Robot for Truss Handling Tasks on the Moon

Dual-arm mobile robot has a good application prospect in various deep space exploration projects in the future. In this paper, a base-arm collaborative planning method for the truss handling task on the moon is proposed. Firstly, the kinematic model of the dual-arm mobile robot is built. For the task of grasping a truss, an optimization model for the location of robot base is built considering better dexterity and lower energy consumption. Then, a pareto solution set of optimal locations of robot base and moving trajectory which is feasible for the task can be calculated by NSGA-II algorithm. Simulation shows that the planning method can greatly improve the dexterity and energy consumption of a task compared with the no optimization method.
Trajectory Planning for Autonomous Driving Based on Spatio-Temporal Corridor

Trajectory planning for autonomous driving is a typical three-dimensional constrained planning problem that takes into account spatio-temporal information. In this work, a trajectory planning method is proposed to solve this highly nonlinear and non-convex problem. First, at all timestamps where the obstacles appear, we decompose the two-dimensional planes consisting of the obstacles and the configuration space, and merge these decomposed planes to construct a series of trapezoidal bodies. Then a feasible spatio-temporal corridor is obtained by filtering the resulting trapezoidal bodies. Finally, the spatiotemporal corridor is projected longitudinally and laterally, then optimized respectively. When performing the longitudinal and lateral optimization, the optimization problem is transformed into a quadratic programming (QP) problem to be sovled. Our work uses the prediction information of obstacles to quickly construct a feasible spatio-temporal corridor. Then we transforms the three-dimensional problem into two two-dimensional QP problems to solve, which can greatly reduce the complexity of the problem and satisfy the conditions of real-time and safety while ensuring global optimality.
Research on Formation Self-healing Method of Multi-agent System Based on Manta Ray Foraging Optimization Algorithm

Multi-agent systems are composed of a series of independent or interacting individuals. Faults are inevitable due to the fact that they often work in complex environments. Fault of agents in a formation can lead to degradation of system performance and efficiency, and if no action is taken, it may even cause serious consequences such as system collapse and formation failure. Therefore, it is important to recover the performance of the formation automatically when fault occues. To address this problem, a self-healing strategy and method based on multi-agent system formation failure is proposed. The optimal solution of current spatial agent distribution is solved by manta ray foraging optimization algorithm, and potential energy function is used to deal with the collision problem during agent movement And the coverage rate is used as an important index of self-healing performance by measure the self-healing effect of intelligent body formation before and after the failure. Finally, the feasibility and effectiveness of the method in the self-healing of multi-agent formation is verified by a large number of simulations.
Event-triggered distributed fusion estimation for multisensor multirate systems with correlated noises and fading measurements

An event-triggered (ET) distributed fusion (DF) estimator is proposed for the multirate multisensor systems with uniform observation sampling rates, correlated noises and fading measurements. In the multirate sampling scheme, the state is updated uniformly and the state update rate is the positive integer multiple of the observation sampling rates. Moreover, the measurement noises of different sensors are correlated and also correlated with the process noise at the same time step. In order to improve the network resource utilization, a variance-based ET mechanism is adopted to determine whether the local estimates are transmitted to the remote fusion center. The matrix weighted optimal DF estimation algorithms is obtained based on the proposed local estimators and cross-covariance matrices. A simulation example is given to illustrate the effectiveness of the algorithm.
Formalizing a Continuous Function in Topological Spaces Using Coq

We present a formalization work in Coq about the continuous functions of topological spaces. The continuity of functions for topological spaces plays an essential role in general topology, which was the origin of rapid developments in general topology. Our formalization work about the continuous functions is based on the system of the axioms of Morse-Kelley, and this axiom system can provide a solid foundation for mathematics. The concepts of continuous function are then introduced and their equivalent definitions are investigated. We transpose the definitions and properties of continuous functions into a formal description using the Coq theorem prover, which demonstrated the rigorous and reliable of formal mathematics. This work shows that the definition of continuous function in topological spaces can also be described by various basic topological concepts. And our formalization work lays a foundation for the formalization of the theory of point-set topology.
Hybrid-A* based Path Planning Algorithm for a Greener Air Traffic Operation

The greenhouse effects are intensifying these days due to population explosion and immoderate industrialization. To reduce emissions, the administration, ATM (air traffic management), and aircraft manufacturers are dedicated to finding a greener air traffic operation mode. Of all the potential solutions, trajectory-based concept that puts forward the capability matching and coordination between aircraft and ATM systems are considered as the most promising one. In this paper, we follow the TBO architecture, and develop a Hybrid-A* based path planning algorithm for large civil aircraft. We first define the planning problem. Hybrid-A* in conjunction with Dubins path are also presented. We then test the proposed algorithm in a simulated 2D workspace. The experimental test results verify the efficacy of the proposed algorithm.
A high-precision measurement system with a large field of view

The sources of measurement errors of stereo vision systems include model approximation errors, data errors, and calibration errors. Due to the existence of these errors, the commonly used binocular self-calibration method is difficult to satisfy the measurement system that pursues high precision. And because the comprehensive index of the size of the field of view and the absolute measurement accuracy is the key to evaluating the stereo vision system. Therefore, this paper designs a vision system with a large field of view and high precision, which is composed of a static surveillance camera and an active gaze camera. And for this system, a joint calibration scheme based on error correction matrix is proposed, which only contains one geometric constraint of ideal rotation axis. Since the nonlinear system of equations formed by these matrices cannot be solved analytically, a numerical optimization solution based on Lie algebra is proposed. Specifically, matrix acquisition is formulated as the problem of minimizing the sum of distance deviations between ideal predicted values and actual observed values. This problem is then solved by differentiating these matrices in the form of left-multiplying tiny perturbations. In this way, a high-precision joint calibration with multi-source errors is achieved. Finally, the effectiveness and robustness of the proposed method are verified by experiments.
Cross-Instance Contrast with Color Decay for Image-Based 3D Shape Retrieval

Image-Based 3D Shape Retrieval (IBSR) has lately attracted appealing interests due to the proliferation of 3D shapes and the maturation of 2D representation learning. However, most of the recent works focus on object shapes with less object color in 2D image representations, which cannot well capture the geometric characteristics. Moreover, the relations between multiple instances which correspond to the same 3D shape are neglected in existing methods, which limits retrieval performance. To tackle these challenges, we propose a Cross-Instance Contrast with Color Decay (CIC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> D) approach for IBSR. Specifically, a straightforward but effective color decay technique is designed to enlarge the color space, which is beneficial for investigating more geometric characteristics. Furthermore, a cross-instance contrastive loss is proposed to build relations between multiple instances for capturing robust 3D representations. Extensive experiments demonstrate that our CIC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> D outperforms advanced methods on three representative benchmark datasets.
Implementation of an Interactive Panoramic Course Learning Platform

Most of the current online teaching platforms use ordinary monocular cameras to capture teaching courses, and many teaching videos just record the offline teaching process and then put them on the online platform. This kind of teaching course is not immersive enough, students cannot get a sense of presence when learning through the online platform, and they cannot get answers in time when they have questions, which will affect the students' learning effect. Compared with the video collected by the ordinary camera, the panoramic video obtained from the panoramic camera has a wider field of view and a better sense of presence and immersion. Our system uses panoramic cameras to collect on-site teaching videos to form 360-degree viewable course videos and upload them to the server for storage. At the same time, a question-and-answer dialogue-assisted teaching robot that can interact with voice is constructed through a server-side dialogue Q&A module. The client is used to display the interactive interface of panoramic video courses and dialog with the question-answering robot teaching assistants. Students can consult the "robot assistant" if they have any questions when watching the course videos through the client. This creates an "immersive" learning atmosphere and avoids repetitive work by the instructor while achieving better learning results.
Multimodal Remote Sensing Image Registration Based on Local Phase Congruency and Keypoint Filtering

Robust registration is able to register the complementary information captured by multimodal images. Owning to the nonlinear intensity differences and repeating patterns in multimodal images, many classic descriptors are unable to achieve good performance. Aiming at this problem, a remote sensing image registration method based on key point description and filtering is proposed. First, this work utilizes a new keypoint descriptor with Log-Gabor filters to characterize the oriented phase congruency information in multimodal images. Then, a keypoint filtering is performed on all descriptors by mean-shift clustering to remove the repeating patterns. This approach is evaluated on three commonly used data sets, which are used for structure map construction and image registration comparisons. The experimental results show that the proposed method is able to preserve highly similar structure features and remove most repeating keypoints in multimodal images, which indicates this method is efficient and can achieve good registration performance.
Memory-Enhanced Gated Graph Attention Network for Next Point-of-Interest Recommendation

The recommendation of the next Point-of-Interest (POI) has become an essential component of location-based social networks (LBSNs). Its purpose is to advise users' undiscovered POIs based on their check-in record. However, the existing work mainly regards the long-term interest of users as a periodic and stable change, but ignores the dynamic change. Besides, sparse POI-POI transformation limits the ability of the model to effectively learn contextual information in local views. We present a memory-enhanced gated graph attention network (MEGGA) in this paper. In order to effectively capture context in a local view, a gated graph attention network (GGAN) is designed, where category information is introduced to alleviate sparsity. In the long-term module, combined with the advantages of RNN and memory network, users' interests are dynamically captured by constantly updating user sequence while processing sequence information. Moreover, we use the attention layer to integrate multiple interests and learn the degree of users depending <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> on different interests. Two public datasets were used in experiments to confirm MEGGA efficacy.
A Mapless Navigation Method Based on Reinforcement Learning and Local Obstacle Map

Recently, research on mapless navigation based on Reinforcement Learning (RL) shows great potential. However, for indoor scenes with "local minimal areas", the results of end- to-end training based solely on RL are not ideal. This paper presents an encoding method for navigation input information, which encodes the single-line lidar information into a Local Obstacle Map (LOMap), and combines the robot speed and target point position information into the neural network. We modify a Safe RL algorithm Constrained Policy Optimization (CPO) for training, and propose a new navigation evaluation metric that considers time-consuming. Simulation experiments on Gazebo show that our proposed method can better adapt to indoor scenes with local minimal areas, and outperforms the CPO method with raw lidar information input in common metrics.
A Study of Path Planning for Multi- UAVs in Random Obstacle Environment Based on Improved Artificial Potential Field Method

In this paper, an improved artificial potential field method is proposed and studied for path planning and obstacle avoidance of multi-UAVs. Based on the traditional artificial potential field method, a dynamic attraction factor and a dynamic repulsion factor are introduced to improve the potential field function and solve the problems of excessive attractive force, path optimization and safety in the obstacle avoidance process, and are applied to multi-UAV path planning. MATLAB simulation is used to demonstrate the feasibility of the improved algorithm in multi-UAV path planning.
Spatio-temporal Channel Feature Shrinkage Network for Skeleton-Based Action Recognition

Recent years, human action recognition based on skeleton data from RGB-D sensors has achieved remarkable performance. However, the accuracy of action recognition is primarily affected by a large amount of noise introduced during the acquisition of human skeleton data and the subsequent feature extraction operation. In this work, we present a new type of Spatio-temporal channel feature shrinkage network (STCSN), which dynamically learns thresholds for practical feature shrinkage across channels and spatiotemporally for eliminating noise in features or irrelevant features. The proposed Spatio-temporal channel feature shrinkage network introduces only a small number of parameters, while delivering significant performance gains. By adding STCSN to a basic graph convolution network, we develop a robust STCSN-GCN graph convolution network that achieves substantial performance gains on the NW-UCLA and NTU RGB+D datasets.
On Optimization Algorithms for Multi-UAVs Search Task

Since Unmanned Aerial Vehicle (UAV) swarm emerged with the merit of high efficiency, flexibility and robustness in search tasks, related research has sprung up in recent years. However, unknown target locations make swarm search a complex mission. In view of this, many optimization algorithms based on UAV swarm have been proposed and improved, which are reviewed and partitioned into non-intelligent algorithms and intelligent ones in this paper. Furthermore, the latter is subdivided into algorithm improvement and algorithm fusion, both of which are set forth at length. At the end of this paper, we analyse the existing challenges and give some insight into the future.
A Robust Motion Planning Algorithm Based on Reinforcement Learning

Path planning is the basis for robots to perform all kinds of intelligent tasks. In this paper, we proposed a path generation algorithm based on reinforcement learning. It first uses the A algorithm, searches for an initial trajectory, generates a series of sub-target points, and then obtains a safe and smooth trajectory based on the SAC reinforcement learning algorithm. Compared to existing trajectory generation algorithms such as DWA, it is possible to adapt to more complex scenarios because it does not need to set the loss function manually. In this paper, simulation experiments are used to prove the practicability of the algorithm. And the proposed algorithm can generate safe and continuous trajectories in real-time.
Anti-candid Camera Detection System Based on Deep Learning

Whether for the commercial secrets of large enterprises or the military secrets, it is vital to keep the relevant confidential documents properly. We propose this anti-candid camera detection system to prevent secret documents from being photographed secretly. We design our system based on deep learning object detection networks to achieve an efficient system for anti-candid camera detection and proper document protection. Our system contains three essential parts: the Mobilenetv2-SSD neural network for object detection, the Hybrid quantization method to get a lightweight network for mobile devices, and model porting to the embedded devices. In the object detection part, the Mobilenetv2-SSD neural network includes the Mobilenetv2 backbone and SSD detection part. Our system achieves 92.4 mAP (mean Average Precision) and 72 fps on mobile device RK3399pro for the Anti-candid Camera Detection task.
A LAB color space style transfer method base on luminance grouping for semantic segmentation

Recently, semantic segmentation has made great progress, which is a basic task in computer vision and assigns the class label for each pixel in a image. Since pixel-level annotation of datasets for semantic segmentation is always a costly task, using the processor-generated virtual image data is an important method to solve this problem. But there are inevitable differences between the synthetic data domain and the reality domain. We propose the following ways to solve the appeal problem. Firstly, a LAB color space style transfer method based on luminance grouping is proposed to reduce the differences between the two domains. Secondly, we found that the Fourier transform spectrum transfer method reduces the difference between the source and target distributions by switching the low-frequency distributions of the source and target domains.
Multimedia-based Camera-Laser Joint Calibration

Line structured light measurement systems are widely used in the 3D model reconstruction in air. When deployed in a multi-media scene, the measurement system will not work effectively due to the failure of general imaging models. To solve these problems, we propose a multi-media-based 3D reconstruction method, in which applying the calibration parameters in the air and considering the refraction of light can improve the accuracy of 3D reconstruction in multimedia without recalibrating sensor parameters. With the help of the system experimental platform, the practicability and accuracy of our proposed method are verified by comparative experiments under common multi-media conditions, respectively.