Reviewerly/exHarmony huggingface dataset:
Vision Based Target Following UGV System Using YOLOv5 and ROS Platform

Realizing the autonomous following function of the unmanned ground vehicle is critical for its applications in the industrial, medical, and home service fields. This paper proposes a visual autonomous following unmanned ground vehicle system for indoor environments based on the ROS platform, which combines autonomous navigation and visual following technology, senses unknown environment with RGBD camera and LIDAR, detects tracking targets with the YOLOv5 algorithm, meanwhile integrates the KCF algorithm and pedestrian re-identification technology for target tracking. The unmanned vehicle system can detect user-specific gestures to start and stop, and during the tracking process, the transfer of different states is realized by a state machine, then the motion control and dynamic obstacle avoidance are implemented using simultaneous localization and mapping (SLAM) algorithm. Finally, the above process is validated using a real unmanned vehicle in the indoor environment, and the results show that the autonomous following UGV system can effectively avoid obstacles and track the target to the specified location, which verifies the availability and reliability of the system.
Cooperative Search Path Planning for Multiple Unmanned Surface Vehicles

Due to the advantages of unmanned surface vehicles (USVs) with wide communication range, faster speed and high flexibility, USV formations are considered suitable for fast target search missions in marine environments, but the problem of how to exploit the clustering advantages of USV formations is still to be studied. In this paper, the multiple homogeneous USV cluster system collaborative search path planning for the underwater moving target is studied, and an improved genetic algorithm based on particle swarm optimization strategy is used for the path planning of the USV system. Firstly, a new concept of credible period interval is added to the USV search process for distinguishing high-security and low-security regions, and then the objectives of information sharing among USVs and joint search area of USVs are added to the fitness function to guide multiple USVs to collaborate more efficiently in performing search tasks. Comparative experiments demonstrate that the multi-USV cluster system collaboration can complete search tasks more quickly than the equivalent number of USV platforms implementing individual searches.
An Efficiently Computable Metric for Multiple Extended Target Tracking

In multiple extended target tracking, the aim is to estimate the positions, shapes and trajectories of extended targets, as well as the number of trajectories. The commonly used metrics are not sufficient to comprehensively evaluate the performance of the multiple extended target tracking. The major challenge of the metric is that it takes into account the similarity of extended targets in the same trajectory over a period of time and the dissimilarity between different target trajectories. In this paper, a new mathematically metric is proposed to jointly measure position, shape and label estimates, which can capture errors such as track separation and switching. The position and shape errors are firstly used to measure the same extended target in continuous time, and then the optimal sub-pattern assignment distance is used to measure the errors between the overall target trajectories. Simulation results verify that the proposed metric can capture differences between two sets of extended target trajectories.
Modified ITO Algorithm for Path Planning of Multi-UAVs

In order to overcome the defects of slow convergence speed and high probability into local optimal solution of the traditional ITO algorithm, a modified ITO algorithm is proposed, which redesigned the adjustment function of ambient temperature and modified the update rule of path weight when the particles drift and fluctuation, so that the particles more accord with the characteristics of Brownian motion. Compared with the other path planning algorithms, the results have shown that modified ITO algorithm has advantages of shorter path length, less time consuming, and accelerated convergence speed. At the same time, the weight coefficients of each factor are linearly adjusted in the optimization process, which ensures the diversity of the initial population and the ability of traversal optimization in the later period, and improves the efficiency and stability of path planning.
Adaptive Guidance Method Based on Distributed MPC of UAV Cluster for Unknown Environment Exploration

UAV cluster plays an important role in large-scale complex task, there are still many challenges like the planning and control quantities cannot be properly matched when facing the exploration of large-scale unknown environment with possible obstacles. This paper proposes an adaptive guidance distributed model predictive control (AG-DMPC) exploration method for unknown complex environment of UAV cluster. The control quantity of UAV is directly planned to avoid the conflict between planning quantity and control quantity in traditional methods. The initial solution is generated based on dynamic constraints, and the rolling optimization solution is carried out through the symbiotic biological search algorithm. The evaluation function is constructed by comprehensively considering the growth of exploration rate, cluster collision avoidance and obstacle avoidance, and the adaptive clustering guidance method is introduced to escape local maximum exploration in trap the search process, so as to realize faster exploration of the environment under the condition of ensuring safety constraints. The simulation results show that this method can effectively avoid collision problems, reduce explore lost and escape local traps to improve exploration efficiency.
UAV Path Planning Based on Rolling Sine-Cosine Harris Hawks Optimization

Aiming at the 3-D path planning problem with an unknown and dynamic environment, this paper propose a novel path planning fusion algorithm by combining the improved sine-cosine Harris hawks optimization(SCHHO) and the rolling window method. Under the unknown environment, make full use of the local environment information detected by the unmanned aerial vehicle (UAV) system in real-time and plan the local path via the improved SCHHO in the rolling window. With “planning multi-steps and moving forward only one step,” the UAV could plan the path when flying. Experimental results showed that the proposed algorithm can effectively plan an ideal path in an unknown environment.
End-to-End Learning-Based Obstacle Avoidance for Fixed-Wing UAVs

In this paper, a deep reinforcement learning training framework based on attitude control is proposed for the obstacle avoidance problem of fixed-wing UAVs in 3D realistic scenes. The training framework includes an environment information feature extraction network and an obstacle avoidance strategy training network. In the training network module, attitude stabilization and altitude maintenance reward and punishment functions are designed for fixed-wing UAV attitude control. The learning of obstacle avoidance strategies is performed while the fixed-wing UAV maintains attitude stability and altitude stability. Finally, the method is validated in the joint simulation environment we built. The training method introduced in this paper and the built training framework provide simulation data and results for the fixed-wing UAV-aware avoidance strategy in a realistic environment, which provides a feasible solution for the deployment of the obstacle avoidance strategy network to real aircraft.
Mission Planning for Heterogeneous UAVs in Obstacle-Dense Environment

In this study, a two-layer algorithm is proposed to solve the mission planning problem of heterogeneous unmanned aerial vehicles (UAVs) in the presence of obstacles. In the upper layer, an improved genetic algorithm (IGA) with specially designed unlocking mechanism is developed to address the task assignment problem. As for the lower layer, a geometric-based method is developed for flight range estimation in the obstacle-dense environment. Three kinds of UAVs with different capabilities, i.e., reconnaissance UAV, combat UAV, and ammunition UAV can be deployed in combination. The maximum flight range among the UAVs, which is proportional to the makespan when different UAVs share the same flight speed, is taken as the objective function to be minimized. Meanwhile, constraints such as precedence, ammunition load, and mission requirements are taken into consideration. Two mission scenarios are carried out to validate the effectiveness and scalability of the proposed algorithm. And sensitivity analysis against different algorithm parameters is explored.
Generating Parameterized Smooth Paths with a Curve-Fitting Algorithm

Path smoothness is essential in path tracking for autonomous driving, while smooth means that the curvature of path is continuous. Parameterizing the path as a profile of $$curvature\ \kappa $$ , that is a function of $$arc\ length\ s$$ , not only simplifies description of the reference path, but also improve path tracking performance. To achieve this, there must be a trade-off between the smoothness of the curvature profile and the precision of the curve-fitting. This paper describes an algorithm which fits the reference path described by a sequence of cartesian points. The algorithm utilizes piecewise linearized $$\kappa -s$$ profile to represent the reference path. The curvature profile is smooth subject to the constraints of lateral deviation.
A Survey of Distributed Architectures and Path Optimization Methods Applied to Clusters of Agents

In recent years, agent clusters have become a research hotspot in the field of multi-agent technology, and can be widely used in post-disaster survivor search and rescue, environmental pollution source tracking and monitoring, park logistics and dispatching and other fields. As an important supporting technology for cluster collaboration, cluster path optimization technology restricts the performance of cluster. This paper summarizes three typical cluster optimization structures, including redundant optimization structure, distributed optimization structure, and hierarchical optimization structure, by analyzing the characteristics of clusters without center, distribution, and self-organization. The work of this paper provides a useful reference for the research of cluster path optimization technology in this field.
Easy to Calibrate: Marker-Less Calibration of Multiview Azure Kinect

Reconstructing a three-dimensional (3D) environment is an indispensable technique to make augmented reality and augmented virtuality feasible.A Kinect device is an efficient tool for reconstructing 3D environments, and using multiple Kinect devices enables the enhancement of reconstruction density and expansion of virtual spaces.To employ multiple devices simultaneously, Kinect devices need to be calibrated with respect to each other.There are several schemes available that calibrate 3D images generated from multiple Kinect devices, including the marker detection method.In this study, we introduce a markerless calibration technique for Azure Kinect devices that avoids the drawbacks of marker detection, which directly affects calibration accuracy; it offers superior userfriendliness, efficiency, and accuracy.Further, we applied a joint tracking algorithm to approximate the calibration.Traditional methods require the information of multiple joints for calibration; however, Azure Kinect, the latest version of Kinect, requires the information of only one joint.The obtained result was further refined using the iterative closest point algorithm.We conducted several experimental tests that confirmed the enhanced efficiency and accuracy of the proposed method for multiple Kinect devices when compared to the conventional markerbased calibration.
Multimodal Transformer with Effective History Information Mining for Vision-Based Navigation with Direct Assistance

The Vision-based Navigation with Language-based Assistance (VNLA) is a recently proposed task that requires an agent to navigate according to a high-level language instruction. Due to the lack of step-by-step navigation guidance, the key to VNLA is to conduct goal-oriented exploration, accompanied by querying an advisor when required to obtain direct/indirect assistance to make progress. However, the goal of general Artificial Intelligence (AI) is still to improve the agent’s autonomy as much as possible. In this paper, we introduce Multimodal Transformer with Effective History Information Mining (MTHM) to address the unique challenges of this task by incorporating long-term history information into decision-makings. Specifically, MTHM leverages real-time visual observations and encoded language instructions to form memory tokens, which are stored in a variable-length memory bank, and uses these rich history clues for exploration. We demonstrate that encoding all the history information with a Transformer is important to solve the VNLA task, and our approach achieves a new state of the art on the ASKNAV dataset.
Cooperative Search Strategy of Multi-UAVs Based on Reinforcement Learning

Aiming at the cooperative search problem of multiple unmanned aerial vehicles for moving targets in unknown areas in a wide area, a cooperative search strategy training method based on reinforcement learning is proposed. This method is based on Markov decision model, centralized training and distributed execution. Through strategy sharing, the dimension of reinforcement learning training process is reduced, and the dynamic environment problem is processed by fusing pheromone model. The simulation shows that the cooperative search training method based on reinforcement learning is not affected by the target movement, and can adapt to different search areas, so that the UAV cluster has the functions of cooperative search in unknown areas and online obstacle avoidance and re planning. Compared with the traditional push sweep search method, the search algorithm based on reinforcement learning training performs better when the search area is wide and the target moves fast.
ORO-YOLO: An Improved YOLO Algorithm for On-Road Object Detection

Environment perception and object detection are the foundation for unmanned ground vehicles (UGV) to autonomously complete various tasks, and also one of the important research topics in the field of unmanned systems. On-Road object detectors based on deep learning have made significant progress, but it is still difficult to balance the detection accuracy and speed. In this paper, an improved fast on-road object detection algorithm ORO-YOLO based on YOLOX network is proposed to solve this problem. First, we improve the backbone network by the reparameterization method to enhance the network feature extraction capability. Second, a feature enhancement module is presented for the actual road target aspect ratio, which enables the network to be more suitable for on-road object detection tasks. Moreover, an adaptive feature fusion module is introduced to replace the original feature pyramid module to further compress the network size. Comparative experiments on KITTI and BDD100k datasets demonstrate the effectiveness of the proposed algorithm, with up to 2% higher accuracy than the original network and about 10% reduction in the amount of network parameters.
Fast Lunar Path Planning Based on Intelligent Heuristic Map

In order to achieve more intelligent and efficient path planning in the complex environment of the lunar surface, this paper uses the lunar surface path generation method based on the intelligent heuristic map. It integrates the benefits of a learning-based end-to-end planning framework with heuristic search ideas. A deep neural network transforms a planning map into an intelligent heuristic map and performs a differentiable heuristic search on that map, enabling the model to perform data-driven path planning on the basis of heuristic search. This ensures the success and completeness of the planning model, as well as the efficiency and generalization of end-to-end planning. The effectiveness of the model is verified by a large number of planning simulation experiments based on the lunar feasible area maps and the lunar elevation maps, and the simulation results show that the method used in this paper can quickly and accurately generate feasible paths in the complex terrain environment of the lunar surface.
Detection and Location System of Dynamic Flying Small Target Based on Vision and Radar Sensor Fusion

Giving the mobile platform the ability to detect and locate targets in real time is one of the keys to make the unmanned system autonomous. This paper presents a high dynamic small target detection, tracking and positioning system, which is composed of a visual camera and a millimeter wave radar. The system uses the target detection method based on deep learning, the target tracking method based on correlation filtering, and the target positioning method based on millimeter wave radar ranging. The system includes two modules, detection and tracking module and ranging and positioning module. The detection and tracking module performs initial detection and continuous tracking of targets based on optical images, while the ranging and positioning module realizes real-time ranging and positioning of small targets, forming a real-time high-precision dynamic flying small target detection and positioning system. Compared with UAV patrol based on ground station, this method realizes autonomous target perception in airborne/vehicle terminal. The operation speed has reached more than 10fps, and the detection rate has reached more than 95%.
Weed Ecologies: a Hybrid Media Experiment on Interstitial Spaces.

Weed Ecologies: a Hybrid Media Experiment on Interstitial Spaces discusses the first of a series of artistic installations within the scope of the practice-based Ph.D. project Tentacular Ecologies: Interspecies Co-creation in the Chthulucene. The practice-based research project explores human and non-human entanglements and collaborations, through an experimental practice at the intersection of Art, Science, and Technology. We investigate the idea of interstitial “third” spaces as potential interfaces, capable of fostering significant ecological relationships between marginalized biological entities and architecture. This paper aims to share the outcomes of our first “tentacular” experiment while introducing its theoretical framework and telling the story of its ongoing process.
Results of geophysical methods in the study of the ancient settlement Dalvarzintepa (Uzbekistan)

The purpose of the study was to use a variety of geophysical techniques to uncover the shape, size, internal structure, and contour of archaeological artifacts buried beneath the surface. Magnetic prospecting, electrical resistivity tomography, and ground penetrating radar were the three main geophysics techniques. Various abnormalities are clearly traced on the maps created from the processing results of all methods in the form of figures with different lengths and forms. Archaeologists were able to identify the most important excavation sites using geophysical study.
829 hectares of preventive archaeological magnetometry surveys in Ireland: upscaling data collection and interpretation strategies

We present examples of preventive archaeology magnetometer surveys at macro scale. Qfield can be used for effective landscape documentation to allow better data analysis, even if done by another team. We show how to improve the reusability of datasets for future Big Data research methods.
The Roman burgus of Trebur/Astheim in FDEMI multi-system data

We present a rare multi-system FDEMI survey at a Roman burgus in Hesse (Germany). The dataset shows the benefits of EMI/ERT system combination for investigating archaeology and embedding paleolandscape.
Monitoring the Internal Status of a Project

It’s much too often that we yield to the temptation of avoiding the internal status monitoring for projects. We have the reports for the clients and meetings with them, so why would we add more work and annoy the people who – in their own words – “want to focus on doing their work.” Then most of the effort goes into making impressive slides for the client. The graphic part and the aesthetic aspects are placed higher than the informative value of the presentations. We end up with pretty slides, tables, diagrams, colorful lights that the audience finds confusing and misleading. The relevant information is not presented in a straightforward manner and the reader must put in a lot of effort into figuring out what it’s all about. Project managers try really hard to hide uncomfortable facts, skillfully avoid explicitly calling problems and risks – all that is caused by fear and embarrassment in front of the client.
A Generic Image Feature Extraction Method Towards Multiple Vision Tasks

With the developments of artificial intelligence and image processing technology, the study of multiple vision tasks has become a hot topic in the field of computer vision, and the key to implementing vision tasks lies in the feature extraction of images. To meet the demands of modern life for multiple vision tasks and promote the deployment of artificial intelligence on edge devices, we propose an image feature extraction method applied to multiple vision tasks, which promotes the deployment of artificial intelligence on edge devices. Firstly, the function of the secondary task in multi-task learning is combined with a feature migration strategy to build a feature migration model for multiple vision tasks. Then, a generic feature extraction model based on feature fusion and feature migration is established under the guidance of image classification. The experimental results show that the proposed feature migration model with image classification as the secondary task can improve the mAP (mean average precision) of the target detection by 5.5%, and the feature migration model with target detection as the secondary task can improve the average accuracy of the image classification by 1%. The proposed generic feature extraction model can effectively implement the image classification and target detection tasks.
Terminology Preferences of the LGBTQ+ Community: A User Study

The LGBTQ+ community is a group with a specific identity where the literature has identified the potential for such mismatches. To investigate the problem of LGBTQ+ terminology from a user’s perspective, 10 LGBTQ+ users of library catalogs were interviewed. Participants were asked to describe an LGBTQ+ title, The Queer Advantage, to identify subject terminology that could be used to query a library catalog, and to react to the title’s assigned Library of Congress Subject Headings (LCSH). Results indicate that participants had a very different focus when describing the title and formulated largely non-expert queries that did not align with the LCSH terminology. They found the LCSH terms to be accurate but academic, and not aligned with their approach. Findings from this study shed light on the ways in which LGBTQ+ users alter their language in interacting with library systems, but at the same time, indicate that the subject terminology used in libraries may not meet the needs of the LGBTQ+ community. Limitations to this study are presented, and further research is explored.