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Location awareness and navigation promote varieties of emerging applications of mobile collaborative multiple uncrewed aerial vehicles (UAVs). Cooperative UAVs fuse the global position system (GPS), inertial navigation systems (INS), peer to peer ranging radios derived from relative navigation of ultra-wideband (UWB) under complicated environments....
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... Nevertheless, this approach is heavily influenced by complex environmental factors and is also constrained by sensor limitations, which significantly restricts its effectiveness. The detection method based on the time-frequency domain characteristics of fault arc current and voltage is currently a more mainstream approach in direct current arc fault detection methods [21][22][23][24]. A large number of domestic and international scholars have conducted extensive research in this field. ...
With the rapid growth of the photovoltaic industry, fire incidents in photovoltaic systems are becoming increasingly concerning as they pose a serious threat to their normal operation. Research findings indicate that direct current (DC) fault arcs are the primary cause of these fires. DC arcs are characterized by high temperature, intense heat, and short duration, and they lack zero crossing or periodicity features. Detecting DC fault arcs in intricate photovoltaic systems is challenging. Hence, researching DC fault arcs in photovoltaic systems is of crucial significance. This paper discusses the application of mathematical morphology for detecting DC fault arcs. The system utilizes a multi-stage mathematical morphology filter, and experimental results have shown its effective extraction of fault arc features. Subsequently, we propose a method for detecting DC fault arcs in photovoltaic systems using a cyclic neural network, which is well-suited for time series processing tasks. By combining multiple features extracted from experiments, we trained the neural network and achieved high accuracy. This experiment demonstrates that our recurrent neural network (RNN) based scheme for DC fault arc recognition has significant reference value and implications for future research. The ROC curve on the test set approaches 1 from the initial state, and the accuracy on the test set remains at 98.24%, indicating the strong robustness of the proposed model.
... This study is motivated and aimed to explore the potential of radio localization for swarm drone implementation, as it is an affordable option compared to a vision system. In addition, radio localization that utilizes Ultra-wideband (UWB) radios are substantially efficient in GPS denied environments for short and immediate-range localization [16][17]. This paper presents an experimental setup for swarming in GPS denied environments as well as obtaining a performance analysis on the system implementation utilizing radio localization, specifically the Loco Positioning System. ...
Swarming is a rapidly growing idea that is being implemented in UAV applications. Its effectiveness and efficiency in finding solutions or executing the desired task served as the main motivation for this study. Localization techniques are vital for swarm implementation and deployment since it one of the main determining factors in its performance. Vision systems have been widely used for localization; however, it may be costly as it requires multiple appropriate cameras. Another localization technique, which is explored in this research, is radio localization. This localization employs Ultrawide-Band radios to communicate with each other to return a target’s position with respect to several reference points. The study presents a new collaborative UAV implementation deployed using radio localized systems for harsh or unknown environments. The study used the Loco Positioning System operating on the Time Difference of Arrival protocol to maneuver two UAVs in a workspace. The study determined how well the system can execute the desired flight path and the performance of the system in keeping the set distance between UAVs to avoid possible collisions. Results of the study showed that the proposed implementation was successful in maneuvering the UAVs flying 0.3 m apart.
... In collaborative team network, message passing is one of the methods that has been exploited to model belief propagation in an uncertain environment [39,40]. A variety of belief propagation approaches has been suggested in the literature, such as Loopy Belief Propagation (LBP), Weighted Loopy Belief Propagation (WLBP), Generalized Belief Propagation (GBP), Nonparametric Belief Propagation, Weighted Loopy Belief Propagation, belief propagation-based dead-reckoning (BPDR) [40], etc. Belief propagation was exploited for variety of applications such as distributed inference [41], cooperative sensing [39], cooperative localization [40,42], collaborative navigation [43], multi-Drone monitoring [44], etc. The performance of these approaches depends on the topology of the graph, as well as the characteristic of the application domain (large-scale applications). ...
Group awareness is playing a major role in the efficiency of mission planning and decision-making processes, particularly those involving spatially distributed collaborative entities. The performance of this concept has remarkably increased with the advent of the Internet of Things (IoT). Indeed, a myriad of innovative devices are being extensively deployed to collaboratively recognize and track events, objects, and activities of interest. A wide range of IoT-based approaches have focused on representing and managing shared information through formal operators for group awareness. However, despite their proven results, these approaches are still refrained by the inaccuracy of information being shared between the collaborating distributed entities. In order to address this issue, we propose in this paper a new belief-management-based model for a collaborative Internet of Drones (IoD). The proposed model allows drones to decide the most appropriate operators to apply in order to manage the uncertainty of perceived or received information in different situations. This model uses Hierarchical Analysis Process (AHP) with Subjective Logic (SL) to represent and combine opinions of different sources. We focus on purely collaborative drone networks where the group awareness will also be provided as service to collaborating entities.
... The ISAC protocol, system architecture design [8], signal-sharing [8][9][10][11][12][13], time-sharing [14], array-sharing [15], spectrum-sharing [16] and power-sharing [17] have been studied to UAV resources but also improves the anti-jamming ability of UAVs from the common frequency ban. • A novel reinforcement-learning-based method is proposed to solve the complex problem, where we formulate a new reward function by combining both the MI and the CR. ...
Due to the limited ability of a single unmanned aerial vehicle (UAV), group unmanned aerial vehicles (UAVs) have attracted more attention in communication and radar fields. The use of an integrated sensing and communication (ISAC) system can make communication and radar modules share a radar module’s resources, coupled with efficient resource allocation methods. It can effectively solve the problem of inadequate UAV resources and the low utilization rate of resources. In this paper, the resource allocation problem is addressed for group UAVs to achieve a trade-off between the detection and communication performance, where the ISAC system is equipped in group UAVs. The resource allocation problem is described by an optimization problem, but with group UAVs, the problem is complex and cannot be solved efficiently. Compared with the traditional resource allocation scheme, which needs a lot of calculation or sample set problems, a novel reinforcement-learning-based method is proposed. We formulate a new reward function by combining mutual information (MI) and the communication rate (CR). The MI describes the radar detection performance, and the CR is for wireless communication. Simulation results show that compared with the traditional Kuhn Munkres (KM) or the deep neural network (DNN) methods, this method has better performance with the increase in problem complexity. Additionally, the execution time of this scheme is close to that of the DNN scheme, and it is better than the KM algorithm.
... UWB localization systems have been widely used in various applications [21]- [24]. However, the UWB range that can be covered is limited by the locations of the UWB anchors. ...
Recently, interest in ultrawideband (UWB)-based localization systems has increased in various application fields. However, since UWB anchors are usually fixed, they work only within the limited range that UWB measurement signals can reach. To address this issue, a swarm flight system with movable UWB anchors is developed. One ground station controls multiple unmanned aerial vehicles (UAVs), each of which is equipped with a UWB anchor and real-time kinematic global positioning system (RTK-GPS) capabilities, and collects the precise (centimeter-level) positions of the UWB anchor UAVs through RTK-GPS. In this way, the constraints of existing UWB systems can be alleviated by changing the UWB anchor positions in real time as desired by the user. In addition, this paper proposes a novel localization algorithm using only time-of-flight (TOF) measurements from UWB signals, heading information from an attitude and heading reference system (AHRS), and altitude information from a barometer. The results of UAV flight tests show that the proposed algorithm provides better localization performance than existing algorithms.
Maximum two-feet distance constraint and ultra-wideband (UWB) technology are commonly used in pedestrian inertial navigation, but they suffer from poor precision and pre-installed anchors. In this work, a multi-person cooperative navigation method based on geometric constraints of foot-to-foot and person-to-person distances measured by UWB is proposed. Combined with gait analysis in kinematics, the gait is divided into the stance and swing phases. For each phase, attitude, position, and the related characteristics of two feet are analyzed and modeled respectively. Furthermore, considering the influence of non-line-of-sight (NLOS) conditions on UWB ranging, the person-to-person distances are formed into the reticulate geometric constraint. An extended Kalman filter is established to estimate the attitude and position of each agent optimally according to the geometric constraints and position vectors. Comparative experiments are designed in various environments. Compared with the traditional inertial navigation system (INS) method, the proposed method improves the end-to-end error by 40% to 70% and the average error by 35% to 72%, respectively. The results show that the proposed method has higher accuracy and better robustness to the environments than the traditional INS method.
The Dynamic Window Approach (DWA) is a popular method for Unmanned Aerial Vehicle (UAV) navigation and localization in unknown environments. It combines Dynamic Programming (DP) with a Probabilistic Route Mapping (PRM) algorithm to provide efficient path planning and obstacle avoidance. DWA can handle a wide range of obstacles, including dynamic and uncertain ones, making it highly reliable. The approach utilizes dynamic programming to compute the optimal path based on the UAV's current state and the known environment. It also employs a hybrid probabilistic route mapping algorithm to estimate the location and movement of unknown obstacles. By combining these techniques, DWA enables the UAV to navigate through complex environments efficiently. One of DWA's key strengths is its ability to handle non-holonomic constraints, such as the limited turning radius of a mobile UAV. It achieves this by defining a dynamic window that determines the feasible set of motions for the UAV at any given time and adjusts the path accordingly. Compared to other popular methods like the Rapidly Exploring Random Trees (RRT) algorithm, DWA outperforms in terms of path planning and obstacle avoidance. It overcomes the limitations imposed by the size of autonomous mobile UAVs by considering the relationship between the robot's dimensions and obstacles in the open space. To enhance sensing and prediction of the surroundings, a laser range finder is utilized in DWA, particularly to handle curved structures or box-canyon formations. This, along with the Dynamic Programming (DP) algorithm, optimizes the path by considering the gathered information. The proposed approach addresses the local minima problem through a strategy to identify the effective path region. Theoretical studies and simulations demonstrate the efficiency and superiority of DWA. In summary, the Dynamic Window Approach is an efficient method for UAV navigation and localization in unknown environments. By combining dynamic programming, probabilistic route mapping, and considering non-holonomic constraints, it provides reliable path planning and obstacle avoidance. Its ability to handle various obstacles, including dynamic ones, sets it apart from other methods, making it highly valuable for UAV applications.
The calibration of anchor positions is the prerequisite for high-precision ultra-wide band (UWB) localization. However the second-order terms of anchor positions error and the distance constraint between anchors are neglected in existent calibration solution, which causes degradation of UWB positioning accuracy. In this paper, a novel calibration and compensation algorithm of anchor positions based on tightly coupled UWB and micro electro mechanical system (MEMS) inertial sensor is presented. First, the trilateral localization model of UWB is established to analyze the impact of anchor positions error on UWB localization performance, it has been demonstrated that the positions error of UWB anchor encompass second-order error terms. To eliminate this error, the second-order measurement model of UWB/MEMS is built through the tight integration of UWB and MEMS, and the nonlinear error of anchor positions is estimated by the derivative unscented Kalman filter (DUKF). Then, the distance between anchors is calculated by the two-way ranging (TWR) strategy, the distance constrained vector is exerted in DUKF to further improve the estimation accuracy of the anchor positions. Finally, experimental results validate the effectiveness of our proposed method, achieving high-precision calibration of anchor positions with a root mean square error of approximately 6cm. This advancement significantly improves the adaptability and reliability of UWB localization systems.
Multi-agent Unmanned Aerial Vehicle (UAV) systems require stable and high-precision navigation. The existing navigation solutions, such as global navigation satellite systems (GNSS) and inertial navigation systems, may perform inefficiently in some application scenarios. The relative navigation methods can help solve this problem. Relative navigation enables UAVs to precisely estimate their positions relative to each other, as opposed to absolute navigation, which calculates the UAVs’ position relative to the Earth. Despite the abundance of relative navigation articles, there are no systematic reviews of relative navigation methods. Additionally, various articles on relative navigation use a variety of terms for comparable concepts, which makes it more difficult to understand the subject. Therefore, this review comprehensively studies systematizes relative navigation methods, and analyzes their strengths and weaknesses. We categorize relative navigation methods appropriate for multi-UAV systems, compare them, and make conclusions based on our findings. The relative navigation methods discussed in this review include differential GNSS, radio-frequency-based, visual, and their combinations. We evaluate the achievable accuracy and range for each type of method according to related studies. We also describe the limitations and vulnerabilities of each method. As a result, we outline relative navigation’s primary capabilities and assess its condition now.
