Reviewerly/exHarmony huggingface dataset:
Railway Track Image Stitching Based on Detail Assistance

Railway track images play an essential role in daily track inspection. However, the limited perspective presented by a single image inherently restricts its applicability. To address this issue, image stitching is applied to obtain images with a large view and enhance the detection efficiency. Most of the traditional image stitching methods utilize purely hand-crafted features for feature matching, while track textures are highly similar, which greatly increases the difficulty of feature extraction in the stitching tasks. In this work, we construct a detail-assisted method for railway track image stitching, which incorporates wavelet transform (WT) and an unsupervised convolutional neural network (CNN). More specifically, considering that railway track images have salient geometric characteristics of edges, WT is employed to extract frequency components of the data; and then CNN is employed to adaptively extract features and accordingly stitch railway track images. Experimental results on our railway image stitching dataset (RISD) verify the excellent performance of the proposed method.
SIEN:Spatial Information Enhancement Network for Blind Super-Resolution

Blind super-resolution methods based on deep learning have attracted increasing interest due to their excellent performance. However, most existing blind SR models underutilize the contextual information of features in LR images, thus relatively reducing the texture details of the images. In this paper, we propose a spatial information enhancement network (SIEN) to address this problem in image blind SR. The network adds a new branch SET module to the idea of alternate iterative optimization of image and blur kernels in blind SR, which acquires global contextual information by making full use of LR image features and restores texture details based on the contextual information. And after extensive experiments, it is proved that our proposed method can have more accurate SR results under the real world.
A Radome Slope Estimation Method Using Multiple-Reference-Model-Based SRUKF Filter

In order to eliminate the influence of radome refraction on the line of sight (LOS) angle, a new radome multiple reference model based SRUKF filter is proposed to estimate the radome slope. The proposed method corrects the measurement of LOS angle by two-step filtering, which can effectively address the issues of insufficient measured data and uncertain variance of the radome slope. Simulation results show that this method can estimate the radome slope effectively, as well as improve the accuracy of the guidance system.
Multi-Agent Cooperation Decision-Making by Reinforcement Learning with Encirclement Rewards

Multi-agent decision-making is increasingly applied on many situations especially on military applications, but their autonomous decision-making ability needs to be improved. Multi-agent Deep Deterministic Policy Gradient (MADDPG) adopts the method of centralized evaluation and decentralized execution, while updates each agent's network parameters based on the global state information rather than only its own state, which can make the entire agent's policy network update in the direction of the global optimum, rather than the individual optimum. In the process of multi-agent cooperation decision-making, the encirclement rewards is introduced to guide agents to make cooperative actions and alleviate the problem of sparse rewards. Firstly, we define the encirclement. By using Graham's algorithm, we find out the effective encirclement of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> agents. We evaluate the encirclement quality from the area of the encirclement and the difficulty of breaking-through for the adversary, and then design the rewards function based on this. Simulation experiments show that the convergence speed and win rate of MADDPG algorithm based on encirclement rewards is significantly improved, and it also has strong adaptability to various task scenarios.
Online Trajectory Generation for Multiple Robots Using Discrete Reference Points in Cluttered Environment

This paper presents a new online trajectory generation method that enables multiple robots to navigate through the cluttered environment. The proposed method first employs a centralized discrete planner to obtain all initial discrete path points with superior deadlock-free performance. And then each robot iteratively updates its subgoals online based on the update strategy and optimizes the trajectory segment by segment until all robots reach the target configuration. The online intermediate subgoal update strategy balances robot body safety and task execution efficiency based on discrete trajectory information and communication exchanges, providing better adaptation to cluttered industrial environments and faster navigation. In addition, the robot's discrete path point information can effectively avoid unsolvable deadlocks and enable the robot to perform transport tasks more efficiently. Finally, simulation experiments validate the effectiveness of the proposed method.
An Environment Adaptation Algorithm Based on RRT for Manipulator Path Planning

Aiming at the problem of slow planning speed and path redundancy caused by repetitive searches of rapidly exploring random trees (RRT), an improved RRT algorithm (EA-RRT) based on environment adaptation strategy is proposed. The algorithm adaptively changes the step according to the vertex environment information around the nearest neighbor vertex to avoid repetitive searches in the same zone. Moreover, the target bias strategy is also introduced, which further accelerates the convergence speed of the algorithm. By comparing the performance of EA-RRT, RRT and RRT-star algorithms through MATLAB simulation and experiments, EA-RRT algorithm significantly improves the path planning speed and reduces path redundancy. Additionally, the effectiveness and practicability of EA-RRT algorithm are verified by robot operating system (ROS) in experiments. Both simulation and experiment have proved that EA-RRT algorithm is superior to the RRT algorithm in terms of operation time and path quality.
Arelative position descriptor for multi-agent reinforcement learning

Since there exists cooperation or competition between the agents in multi-agent systems, relative position based information becomes very important for agents to learn collaborative behaviors. In this work, we propose a relative position descriptor to present the neighborhood relations of each agent. The relative position descriptor divides the neighborhood space of an agent into several bins according to the bearing angle, and the bins are utilized to store task-related information. The length of the relative position descriptor is fixed, so it is well suited for scenarios where the number of agents is large or changeable. We evaluate the proposed relative position descriptor on several multi-agent tasks. The experimental results illustrate that the relative position descriptor can improve the performance of multi-agent reinforcement learning.
Information Correlation Co-Supervision Based Graph Convolutional Clustering Model

Graph clustering aims to divide the graph nodes into distinct clusters in an unsupervised manner, which usually encodes the data features and the corresponding graph structure into a latent feature space. However, we observe that the node representations learned by existing graph embedding methods are always of poor quality and may contain noise, e.g., redundant information or deficient information, which significantly affects the clustering performance. To solve this problem, we propose a novel Information Correlation Co-Supervision based Graph Convolutional Clustering Model. This model consists of a hierarchical deep network architecture, i.e. graph auto-encoder and auto-encoder. Specifically, the graph convolutional auto-encoder (GCAE) is utilized to integrate the graph structure information into the latent feature space, and the auto-encoder (AE) is proposed to learn node embedding. In order to make the node representations more discriminate and more robust against noise, the information correlation co-supervision loss is designed to supervise the correlation of node representations learned by AE and GCAE. Extensive experiments on three public datasets demonstrate that our proposed model performs better than the multiple state-of-the-art deep clustering method.
Dynamic Obstacle Avoidance for UAV Motion Planning in Unknown Environments Based on Probabilistic Inference

Safe, agile, and reliable Unmanned Aerial Vehicle (UAV) autonomous navigation in the unknown environment requires the motion planning module's fast reaction to the potential collision caused by obstacles. However, avoiding dynamic obstacles remains a challenge in most existing work. In this paper, we propose a modified dynamic rapidly exploring random tree star (RRT*) algorithm to obtain a fine time allocation initial solution connected to the goal state while reducing sampling failures. We further optimize the solution by probabilistic inference-based Gaussian Process Motion Planning (GPMP2), which infers a feasible trajectory on the factor graph, and our method uses a novel dynamic obstacle factor to adapt the predicted information's uncertainty. Finally, the simulation results demonstrate our method's capability to travel in unknown environments when dynamic obstacles appear.
SCI Fusion Descriptor Fractional Order Kalman Filter

For the descriptor fractional order system of some canonical type, the singular value decomposition method is used to transform the singular system into two equivalent non-singular subsystems, and then the local single-sensor state filter is given. On this basis, the sequential covariance intersection (SCI) fusion fractional order Kalman filter is proposed. Compared with the existing track fusion descriptor fractional order Kalman filter, the proposed SCI fusion descriptor fractional order Kalman filter has higher accuracy. The effectiveness of the proposed algorithms was verified by simulation.
Full-Dimensional Collision Avoidance of Autonomous Vehicles Using Sequence Convex Programming

A vehicle trajectory planning strategy for autonomous collision avoidance is proposed in this paper. First, a non-convex optimization model for vehicle trajectory planning is established. Using strong duality of convex optimization, the Lagrange dual transformation is performed, and the nondifferentiable collision avoidance constraints are transformed into smooth, differentiable constraints. Then, by using variable substitution and convex approximation, the nonlinear optimization problem is transformed into a convex optimization problem. After the discretization and relaxtion of the convex optimization subproblem, the sequential convex optimization (SCP) method is used to solve the problem. Finally, the effectiveness of this method is verified by numerical simulation. The results show that the SCP improves solution efficiency of the optimization problem under the premise of achieving similar performance. The algorithm shows a satisfying performance on the scenes of parallel parking and reverse parking compared with traditional approach.
A Robust Variational Bayesian Student-T CKF Algorithm for Hypersonic Vehicle Tracking

A robust variational Bayesian Student-t Cubature Kalman filter (RVBSTCKF) algorithm is proposed in this paper to address the difficulties arising from the tracking for the complex maneuvering forms of hypersonic vehicle under the non-stationary heavy-tailed measurement noise with the unknown statistics. Firstly, the Student-t (ST) distribution is introduced to approximate the measurement noise of hypersonic vehicle which is caused by non-stationary outliers in complex flight environment. In addition, the inverse Wishart distribution and the Gamma distribution are introduced to improve the ST distribution, which are implemented to approximate the uncertain covariance and non-Gaussian degree, respectively. Moreover, on the basis of the improved ST distribution, the explicit expressions of posterior update can be derived analytically in nonlinear system by the introduction of Cubature Kalman filters (CKF) and variational Bayesian (VB) algorithm. The simulation results demonstrate that the proposed algorithm has the better estimation accuracy on the tracking of hypersonic vehicle.
The Treachery of Images: Bayesian Scene Keypoints for Deep Policy Learning in Robotic Manipulation

In policy learning for robotic manipulation, sample efficiency is of paramount importance.Thus, learning and extracting more compact representations from camera observations is a promising avenue.However, current methods often assume full observability of the scene and struggle with scale invariance.In many tasks and settings, this assumption does not hold as objects in the scene are often occluded or lie outside the field of view of the camera, rendering the camera observation ambiguous with regard to their location.To tackle this problem, we present BASK, a Bayesian approach to tracking scale-invariant keypoints over time.Our approach successfully resolves inherent ambiguities in images, enabling keypoint tracking on symmetrical objects and occluded and out-of-view objects.We employ our method to learn challenging multi-object robot manipulation tasks from wrist camera observations and demonstrate superior utility for policy learning compared to other representation learning techniques.Furthermore, we show outstanding robustness towards disturbances such as clutter, occlusions, and noisy depth measurements, as well as generalization to unseen objects both in simulation and real-world robotic experiments.
A Pedestrian Object Detection Method from Point Cloud Based on Skeleton-Aware Network

This work addresses the problem of low accuracy in the object detection of pedestrians from point cloud in road scenes, a detection method based on human skeleton-aware network is proposed. The whole framework is based on the two-stage object detection method PointRCNN. The stage-1 sub-network uses DGCNN to segment the foreground points of the whole scene point cloud, and generates 3D proposals from foreground points. The stage-2 sub-network applies the topology and the prior information of the human body model to pre-train a skeleton-aware network to capture the body shape and posture information hidden in the human point cloud and estimates the parameters of posture for accurate box refinement. Experiments show that our 3D detection method achieves better performance than other classic methods on pedestrian object detection.
Optimizing Task Allocation in Nuclear Accidents Rescue Response Using Particle Swarm Optimization

The environment after the nuclear power accident is complex, and it is difficult for rescue workers to enter the accident area safely and effectively rescue the survivors. The robot can replace the rescue workers to complete the rescue work efficiently. On this basis, a task allocation method for emergency multi-robot after nuclear power accident is proposed. First, according to the position and time window constraints of the task, a task sequencing method is proposed, and the inferior solution is eliminated according to the robot's ability constraints when generating the initial solution, so as to reduce the computational complexity. Then, considering that the task and robot number are nominal variables and discrete, a particle swarm optimization iteration method based on roulette wheel is used to solve the task assignment problem based on grouping. The experimental results show that this method can effectively shorten the rescue path and speed up the generation of task assignment scheme.
LGEGAN: A Lightweight Evolutionary Generative Adversarial Network with Statistic Global Information

Generative Adversarial Networks (GANs) have been applied in many fields. However, the existing GANs and their variants encounter many problems, including pattern collapse, training instability and falling into local optimal. Therefore, we construct a lightweight evolutionary generative adversarial network (LGEGAN) with statistic global information. To solve the problem that shallow convolutional neural networks are difficult to capture long-range feature dependence and the training process is prone to pattern collapse, LGEGAN is different from EGAN in that we add an improved self-attention mechanism to the generator network. To solve the problem of instability in the training process, we add spectral normalization to LGEGAN, which increases the stability of each generation of training process. Finally, to efficiently evolve individuals who adapt to the environment in a short time and solve the problem of falling into local optimal, different from EGAN, we construct a novel selection operator and apply it to the selection stage of LGEGAN's evolution of the generator. In the experiments, we evaluate the LGEGAN from four aspects: the quality and diversity of generated images, the collapse of patterns, the stability of training and the robustness of the architecture. The experimental results indicate that the performance of LGEGAN is better than that of EGAN, MOEGAN, SMOEGAN, LRGAN, ProbGAN and the other generative adversarial network models.
Sensor Placement Optimization and Rapid Deployment of a Class of Visual Sensor Networks

To achieve the purpose of large-scale visual tracking of targets based on Visual Sensor Networks (VSNs), this paper proposes a sensor placement optimization method to provide a suitable sensor configuration that satisfies the requirements of maximal network coverage and minimal reconstruction error as closely as possible. In the proposed method, some necessary models are established as the mathematical basis of the optimization process. Then, a multi-objective optimization function is designed to balance the coverage and reconstruction error, followed by an intelligent optimization algorithm that combines the advantages of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Additionally, a dimensionality reduction technique is used to represent the sensor location information using a one-dimensional forward distance, which makes the search process simpler and increases execution speed. Finally, comprehensive comparison experiments are carried out in various settings to show the reliability and effectiveness of the optimization algorithms.
Multi-Agent Path Finding for Non-Conflicting Paths: An Infinite Speed Conflict-Based Search Approach

Multi-Agent Path Finding (MAPF) is the problem of finding a set of non-conflicting paths for multiple agents on a graph, which is NP-hard to solve optimally. This paper proposes an infinite speed Conflict-Based Search (IS-CBS) algorithm to solve the MAPF problem when the agents move at different speeds and the edges correspond to roads of varying lengths. In addition, three distinct approaches are introduced to calculate admissible heuristics for IS-CBS: the sum approach which uses the sum of all heuristics as the final heuristic, the Pareto approach which builds a Pareto set and selects nodes from it, and the alternation approach which alternates between heuristics in search iterations. In our experiment, IS-CBS with heuristics outperforms IS-CBS in terms of success rate, runtime, and number of expanded nodes which is reduced by up to a factor of 21.
Aspect-Level Sentiment Analysis Based on Disentangled Attention and Aspect Proximity Weights

Aspect-based Sentiment Analysis (ABSA) aims to analyze the sentiment polarity of different entities, and it is a fine-grained sentiment classification task. The advent of pre-trained language models has significantly improved the performance of many natural languages processing tasks, and BERT-based pre-trained language models have been successfully applied to tasks that require a deep understanding of language, such as sentiment classification. However, the previous approaches rely on the semantic relevance of a word with a large range of its context. This may lead to an undesirable result. One aspect will go to focus on another aspect's context and confuse the judgment of the model. Although the best ABSA models have achieved significant performance, they still have problems with robustness. Considering this, we choose DeBERTa (Decoding-enhanced BERT with disentangled attention), which uses disentangled attention in the calculation of attention score, and pays more attention to the relationship between text content and position. Besides, we propose aspect proximity weights (APW), which are designed for the relative distance between aspects and their context. The aspect proximity weight is a kind of weight designed for the relative distance between aspects and their context. The experimental results on the SemEval2014 dataset and the ARTS dataset show that the DeBERTa combining aspect proximity weights has better performance and robustness compared with other baseline models.
Hardware Implementation of On-Chip Learning Neural Network on FPGA

Animals achieve precise locomotion through learning and practice, and many scholars are investigating how animals acquire new skills. Deep Reinforcement Learning (DRL) is often employed in skill learning and locomotion in robotics. However, the relationship between the learning process and neural networks or DRL remains unclear. In this paper, we propose a framework of backpropagation (BP) network based on matrix multiplication, which is implemented and visualized on a Field Programmable Gate Array (FPGA). This allows us to track changes in each neuron by analyzing nonlinear signals. By considering each register in the FPGA as a neuron, we can plot the weights of joints, which provides insights into the learning rules.
Three-Dimensional UAV Path Planning Based on Improved Constrained State Transition Algorithm

Unmanned aerial vehicles (UAVs) have been widely used in various fields. The UAV path planning is an important part of the UAV autonomous control system. However, the results of the traditional UAV path planning methods are unsafe and infeasible in complex three-dimensional flight environment. In this paper, a novel UAV path planning method based on improved constrained state transition algorithm is presented for solving this problem. In the proposed method, the three-dimensional UAV path planning in complex flight environment is constructed as a constrained optimization problem. The improved constrained state transition algorithm with mutation scheme (ICSTA) is proposed, which has an excellent global search capability and maintains the diversity of the population. The proposed method based on ICSTA obtains a safe and feasible optimal flight path in the simulation, whose superior performance is also verified.
Perception-Aware Trajectory Planning of Quadrotor in SE(3)

Recent achievements in trajectory planning enable quadrotors to navigate autonomously in unknown complex environments. However, active adjustment of the limited field of view(FOV) remains a major challenge in trajectory planning, and existing methods rarely consider quadrotor visibility into unexplored spaces. This paper proposes a Perception-aware local trajectory planning framework considering limited FOV constraints, which enhances the quadrotor's observation of waypoints on the trajectory. In contrast to existing work, this paper designs a path search with an optimistic FOV constraint and matches the observed relations between waypoints. Trajectory optimization is performed in SE(3) and takes into account the metric of limited FOV constraints and avoidance of obstacle occlusions between waypoints with observation relationships. These metrics are formulated as differentiable cost functions to actively observe future trajectories. Physical simulation and actual flight experiments verify the effectiveness of the proposed method.