JournalTOCs

Unmanned Systems
Number of Followers: 4

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2301-3850 - ISSN (Online) 2301-3869
Published by World Scientific

[121 journals]

Research on Aircraft Direction Finding Based on Antenna Directionality
Parameter Identification
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  Authors: Xinmin Tang, Shengjia Tang, Junwei Gu, Xiangmin Guan, Weili Yuan
  Pages: 1 - 14
  Abstract: Unmanned Systems, Ahead of Print.
  Radar direction finding is an essential cooperative monitoring method for aircraft, and the directional parameters of the direction-finding antenna determine the accuracy of direction finding. A modified amplitude comparison direction finding model is proposed based on the traditional principle of adjacent amplitude comparison direction finding and the error of amplitude comparison direction finding. Based on the modified amplitude comparison direction finding model, an error correction genetic simulated annealing (COR-GSA) algorithm is proposed to identify unknown directional antenna directionality parameters by determining the antenna directionality parameters that need to be identified. Identification experiments were conducted using a self-designed dual channel DF receiver, and 1000 sets of aircraft real azimuth data were used for verification. The results showed that using the COR-GSA algorithm to identify antenna directional parameters had the highest DF accuracy. Finally, the identified antenna directionality parameters were used to track the aircraft’s azimuth, and the tracking error was reduced by 18.3% compared to the tracking error without azimuth correction.
  Citation: Unmanned Systems
  PubDate: 2024-08-27T07:00:00Z
  DOI: 10.1142/S2301385025500542
Lateral Maneuvering with a UAV Mitigating Lateral CG Variations: Modeling
and an Efficient Adaptive Backstepping Control
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  Authors: Anukaran Khanna, Bijoy K. Mukherjee
  Pages: 1 - 17
  Abstract: Unmanned Systems, Ahead of Print.
  In this paper, an adaptive backstepping-based control scheme is proposed to perform autonomous lateral maneuvers under significant lateral offset in the center of gravity (c.g.) position in a UAV. It is first shown that the coupled equations of motion arising from lateral c.g. shift can be simplified and cast in block strict feedback form making it amenable to a two-step backstepping control design. Useful nonlinear terms in the equations of motion are identified and retained in the backstepping design to ensure a less conservative control. Adaptation law is incorporated to dynamically adjust to changes in the c.g. position by adding an adaptive term to each step of the backstepping control. Lyapunov’s direct method and LaSalle’s invariance principle are applied to establish asymptotic stability of both tracking errors and errors in the c.g. estimate. To validate the effectiveness of the proposed control strategy, simulation results for horizontal turn maneuver are presented for the fixed wing Aerosonde UAV and maneuver performance is observed to remain highly insensitive to a wide range of lateral c.g. positions on either side of the fuselage centerline. Furthermore, a comparative control performance analysis is carried out against an ad-hoc model-based adaptive backstepping control scheme available in the literature and the results show significant performance enhancement in the proposed scheme. Along with the c.g. variations, the effects of steady crosswind are also investigated and the control formulation is modified to mitigate these effects too. Real-time control hardware in loop simulations are also provided in support of the real time viability of the proposed control.
  Citation: Unmanned Systems
  PubDate: 2024-08-22T07:00:00Z
  DOI: 10.1142/S2301385025500554
Effectiveness of Iterative Learning Control for Vision-Based Tracking in
Repeated Tasks Under Varying Lighting Conditions
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  Authors: Lattapol Thurnim, Benjamas Panomruttanarug
  Pages: 1 - 14
  Abstract: Unmanned Systems, Ahead of Print.
  Among available techniques for lateral tracking in autonomous driving, none possesses the capability to learn from past behaviors and progressively reduce lateral errors. In contrast, our proposed iterative learning control (ILC) scheme significantly enhances tracking performance in vision-based classical control, particularly in challenging environmental conditions. Our vision-based control system incorporates real-time semantic segmentation using the ENet model for unconstructed road area extraction. Linear regression estimates steering adjustments, while a visual PID controller maintains the vehicle’s position at the road’s centerline. This control system’s performance varies with lighting conditions, notably in dense shade, where the vehicle tends to deviate from the desired path. To assess ILC’s potential in error reduction over successive trials, we examined various ILC structures and compared them with a pure PID controller. Despite challenges in directly comparing PID and ILC designs due to changing lighting conditions during experiments, ILC consistently reduced tracking errors and improved path alignment with each iteration. Notably, the degree of error reduction became more pronounced with a greater number of learning gains in the ILC design. Our experimental results underscore the overall effectiveness of ILC in tracking the desired path in autonomous driving scenarios, particularly in varying environmental conditions.
  Citation: Unmanned Systems
  PubDate: 2024-08-20T07:00:00Z
  DOI: 10.1142/S2301385025500505
[math]-Nash Equilibrium of Pursuer–Evader–Defender Missile
Navigation Dynamic Games
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  Authors: Sebastian Noriega-Marquez, Alejandra Hernandez-Sanchez, Isaac Chairez, Alexander Poznyak
  Pages: 1 - 23
  Abstract: Unmanned Systems, Ahead of Print.
  This research is dedicated to developing a min–max robust control strategy for a dynamic game involving pursuers, evaders, and defenders in a multiple-missile scenario. The approach employs neural dynamic programming, utilizing multiple continuous differential neural networks (DNNs). The competitive controller devised addresses the robust optimization of a joint cost function that relies on the trajectories of the pursuer–evader–defender system, accommodating an uncertain mathematical model while adhering to control restrictions. The dynamic programming min–max formulation facilitates robust control by accounting for bounded modeling uncertainties and external disturbances for each game component. The value function of the Hamilton–Jacobi–Bellman (HJB) equation is approximated by a DNN, enabling the estimation of the closed-loop formulation for the joint dynamic game with state restrictions. The controller’s design is grounded in estimating the state trajectory under the worst possible uncertainties and perturbations, providing a robustness factor through the robust neural controller. The learning law class for the time-varying weights in the DNN is generated by studying the HJB partial differential equation for the missile motion for each player in the dynamic game. The controller incorporates the solution of the obtained learning laws and a time-varying Riccati equation, offering an online solution to the control implementation. A recurrent algorithm, based on the Kiefer–Wolfowitz method, adjusts the initial conditions for the weights to satisfy the final condition of the given cost function for the dynamic game. A numerical example is presented to validate the proposed robust control methodology, confirming the optimization solution based on the DNN approximation for Bellman’s value function.
  Citation: Unmanned Systems
  PubDate: 2024-08-20T07:00:00Z
  DOI: 10.1142/S2301385025500517
DMTM: Fast Obstacle Detection and Tracking for Motion Planning
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  Authors: Ali Analooee, Amin Nikoobin
  Pages: 1 - 20
  Abstract: Unmanned Systems, Ahead of Print.
  This paper presents an obstacle detection and tracking framework for mobile robots and autonomous vehicles equipped with LiDAR (Light Detecting and Range finding) sensors. The framework contains a detection module (DM) for clustering the point cloud and modeling the obstacles, and a tracking module (TM) for recognizing the obstacles and estimating their velocities. In order to detect the obstacles, DM segments the point cloud by finding the gaps in it. Detected obstacles are modeled by one or two line segments depending on their geometry. TM gets the line segments returned by DM as the obstacles to track. To this end, first, the obstacles are labeled and their features are stored. Thereafter, a set of equations are solved to recognize the labeled obstacles. Finally, the Kalman filter is used to calculate the translational and rotational velocities of the obstacles. The framework is evaluated in experiments on a robot platform and using the KITTI dataset. The results indicate satisfactory performance in terms of effectiveness and quickness and confirm that DM and TM are qualified enough to perform in real time as ancillary modules of mobile robots and autonomous vehicles. Especially, because of robust and accurate obstacle modeling, the velocity diagrams are smooth and coherent, a point which is not seen in similar researches.
  Citation: Unmanned Systems
  PubDate: 2024-08-13T07:00:00Z
  DOI: 10.1142/S2301385025500566
HGEE: Learning for Trajectory Prediction with Heterogeneous Graph
Interaction and External Embedding of Unmanned Swarm Systems in
Adversarial Environment
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  Authors: Peiqiao Shang, Zhihong Peng, Hui He, Wenjie Wang, Xiaoshuai Pei
  Pages: 1 - 14
  Abstract: Unmanned Systems, Ahead of Print.
  Trajectory prediction of unmanned swarm systems, serving as the foundation for behavioral and intentional cognition, has attracted extensive attention and made considerable progress in adversarial research. The influence of heterogeneous interaction relationships and external factors is crucial for trajectory prediction. Consequently, this paper proposes the Heterogeneous Graph with External Embedding (HGEE) network. We model the latent variables as multi-layer heterogeneous graphs based on prior knowledge of different interaction relationships and propose a method for calculating edge embeddings for heterogeneous graphs. Furthermore, we introduce a method that combines external environmental feature with historical observational trajectory data as the input for the decoder, enabling the model to learn the impacts of obstacles, targets, and desired formations on trajectories. We demonstrate that our approach surpasses state-of-the-art models in interaction inference and trajectory prediction through experiments on our proposed formation datasets based on consensus theory, across five evaluation metrics.
  Citation: Unmanned Systems
  PubDate: 2024-08-09T07:00:00Z
  DOI: 10.1142/S2301385025500530
Best Individual Guided Immune Plasma Algorithm on Solving Path Planning
Problem of Unmanned Combat Aerial Vehicles
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  Authors: Selcuk Aslan, Fatma Ozge Ozkok
  Pages: 1 - 23
  Abstract: Unmanned Systems, Ahead of Print.
  Unmanned aerial vehicles (UAVs) and their variants equipped with sophisticated weapon systems called unmanned combat aerial vehicles (UCAVs) have completely changed the classical war strategies and concept of military operations. For guaranteeing the autonomous flight safety and success of the task being performed by these modern aerial vehicles, a path must be determined optimally after considering some kinematic constrains, existence of enemy threats, fuel or battery limitations. Immune plasma algorithm (IP algorithm or IPA) inspired by the implementation steps of a medical method gained popularity with the COVID-19 and known as convalescent or plasma treatment is one of the most recent intelligent optimization or meta-heuristic techniques. In this study, plasma treatment procedure of the IPA was changed with a newly introduced approach called the best individual guidance for short BIG that is based on using the most qualified solution found by the algorithm and three different donors when collecting plasma and BIGIPA was developed as a novel UCAV path planner. For investigating the path planning capabilities of the BIGIPA, a set of detailed experiments was carried out by using different battlefield configurations and assigning various constants to the control parameters such as population size and number of receivers and then obtained results were compared with the results of other path planners based on well-known meta-heuristic algorithms. Experimental studies showed that introduced treatment procedure gives a significant contribution to the convergence performance and qualities of the final solutions especially for the test cases with relatively high dimensionalities and BIGIPA calculates more promising, flight efficient, and safe UCAV paths compared to the tested algorithms.
  Citation: Unmanned Systems
  PubDate: 2024-07-30T07:00:00Z
  DOI: 10.1142/S2301385025500475
Curriculum Reinforcement Learning for Autonomous Planning in Unprotected
Left Turn Scenarios
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  Authors: Yuzhen Zhu, Shuyuan Xu, Xuemei Chen, Yanan Zhao, Xianyuan Dong
  Pages: 1 - 15
  Abstract: Unmanned Systems, Ahead of Print.
  In complex urban scenarios like intersections without dedicated left-turn signals, the construction of planning systems that maximize efficiency while guarantee safety has been a significant challenge. In this paper, we propose a reinforcement learning approach based on curriculum learning using real world dataset, and we develop a partial end-to-end planning and control model capable of adapting to variable temporal and spatial dimensional state inputs, applying it to autonomous driving task. Our model is compared with mainstream reinforcement learning algorithms to validate that our proposed algorithm can effectively solve complex spatio-temporal planning problems. This significantly enhances the efficiency of passing while maintaining a certain level of safety.
  Citation: Unmanned Systems
  PubDate: 2024-07-20T07:00:00Z
  DOI: 10.1142/S2301385025410018
Monocular Vision Sensor-Based Indoor Road and Stair Detection
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  Authors: Pengzhan Liu, Wei Jin, Chunhua An, Jianqiang Zhao
  Pages: 1 - 11
  Abstract: Unmanned Systems, Ahead of Print.
  Road detection is an essential component of indoor robot navigation. Vision sensors have great advantages in road detection as they can provide rich information in terms of environmental perception. In this paper, a monocular vision sensor-based method for indoor road and stair detection is proposed, which detects feasible areas in indoor environments very fast without paying attention to detailed features of walls or other obstacles. More specifically, for a given indoor road image captured by an on-board vision sensor, the simple linear iterative clustering (SLIC) algorithm-based approach for efficient image segmentation is introduced. Then, according to the DBSCAN algorithm, the generated superpixels are clustered to form large areas of view. The initial road area is obtained through a safe window on the middle bottom of the image. In order to achieve a more accurate road segmentation, the initial image is processed by the binary search, edge detection based on the Canny operator and straight-line detection and location based on the Hough transform, which integrates edge and stair information into road detection. Several experiments are performed to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed method could accurately detect road information and staircase information in images and succeeds in addressing the indoor road-detection problem.
  Citation: Unmanned Systems
  PubDate: 2024-07-16T07:00:00Z
  DOI: 10.1142/S2301385025500499
Small Object Detection via Scale-Adaptive Label Assignment and
Localization Uncertainty
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  Authors: Hui Qin, Tiancan Mei, Yaru Wang
  Pages: 1 - 11
  Abstract: Unmanned Systems, Ahead of Print.
  Despite the current detectors achieving outstanding performance, detecting small objects remains a challenging problem. The challenge mainly arises from the low quantity and quality of samples as well as the inherent difficulty in localization. Focusing on these problems, we present an approach for small object detection with a scale-adaptive label assignment scheme and a novel quality-driven localization loss (QLL). First, we perform the scale-adaptive label assignment by combining distance-based and Intersection-over-Union (IoU)-based criterion along with a scale discriminator mechanism to obtain larger quantity and higher quality of training samples. Then, we extend an additional branch parallel to the original localization branch, modeling the localization task as predicting Gaussian probability distributions to incorporate localization uncertainty. Finally, we develop QLL by integrating the scale information and IoU to achieve more accurate localization for small objects. Extensive experiment results on two natural images benchmarks demonstrate that our method underscores its superiority over baseline detector in detecting small objects. Moreover, our method performs better than other recent state-of-the-art methods on the large-scale small object detection benchmark SODA-D without bells and whistles.
  Citation: Unmanned Systems
  PubDate: 2024-07-11T07:00:00Z
  DOI: 10.1142/S2301385025500463
Aerodynamic and Structural Design Procedures Supported by Fabrication and
Flight Testing of a Small Unmanned Helicopter
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  Authors: Amr Mansour, Mohammed Kassem, A. M. Abdel-Rahman, Mohamed Y. Zakaria
  Pages: 1 - 28
  Abstract: Unmanned Systems, Ahead of Print.
  In this work, typical design, production, and testing procedures for a small unmanned helicopter are explained and performed. In doing so, preliminary sizing of the helicopter and three main disciplines are conducted: aerodynamic analytical and numerical simulations, power calculations, and structure analysis assessment. First, a thorough survey is implemented to obtain the trends for the maximum take-off weight versus some design constraints such as rotor diameter, motor power, payload, and empty weight. Performance calculation results are obtained to figure out all aspects that correspond to the specified mission. The designed rotor geometry along with the aerodynamic characteristics and flight performance variables is then validated using the blade element theory and numerical simulations. Second, based on the power curves obtained for different flight regimes, an electric brushless motor is selected. The numerical simulations (Computational Fluid Dynamics) analysis is used to enhance the selection which implies that the motor power should be greater than 5.4 kW to overcome the drag forces. The motor power selection corresponds to a maximum rotor pitch angle of 15∘ and a maximum rotor speed of 1450 RPM. Then, the aerodynamic loads are used as an input for the structural analysis using one-way coupling of fluid–structure interaction (FSI) and consequently designing the internal structure of the blade. Eventually, the internal structure manufactured using carbon fiber-reinforced polymer (CFRP) by applying a combined technique between wet layup and compression molding. The blade is statically tested compared with numerical finite element model results. The fuselage structure along with hub and tail units is manufactured and assembled with the existing on-shelf components to examine the helicopter lift capability with different payloads up to 9 kg. The results show that the detailed design process is significant for manufacturing such blades and the helicopter is capable of lifting off the ground with various payloads depending on the rotor pitch angles (8∘, 12∘, and 15∘) at a constant rotor speed of 1450 RPM.
  Citation: Unmanned Systems
  PubDate: 2024-07-06T07:00:00Z
  DOI: 10.1142/S230138502550044X
Adaptive Fuzzy Sliding-Mode Tracking Control for a VTOL Aircraft System
Under Predefined Performance
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  Authors: Minglong Zhou, Xiongfeng Deng
  Pages: 1 - 11
  Abstract: Unmanned Systems, Ahead of Print.
  The predefined performance tracking control issue of a vertical take-off and landing (VTOL) aircraft system is discussed in this work. Employing the presented control performance function, the constrained error signal of the aircraft is switched into an equivalent unconstrained signal. Moreover, fuzzy logic systems are introduced to address unknown nonlinear dynamics problems in aircraft system. Furthermore, in order to address the approximation errors and the external disturbances of aircraft system, parameter-adaptive update laws are proposed in the analysis process. Subsequently, adaptive fuzzy sliding-mode control laws are designed by combining fuzzy control method with sliding-mode control technique. By applying the redesigned sliding-mode functions and the Lyapunov stability method, the output of the VTOL aircraft system can track the desired trajectory, and the tracking errors are constrained in a predefined range. Finally, the availability of the proposed control laws is testified through simulation examples.
  Citation: Unmanned Systems
  PubDate: 2024-07-04T07:00:00Z
  DOI: 10.1142/S2301385025500487
3D ToF LiDAR for Mobile Robotics in Harsh Environments: A Review
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  Authors: Tao Yang, Jinwen Hu, You Li, Cheng Zhao, Li Sun, Tomas Krajnik, Zhi Yan
  Pages: 1 - 23
  Abstract: Unmanned Systems, Ahead of Print.
  Over the past decade, the use of 3D Time-of-Flight (ToF) LiDARs in mobile robotics has grown rapidly. Based on our accumulation of relevant research, this paper systematically reviews and analyzes the use of 3D ToF LiDARs for mobile robotics under harsh conditions such as adverse weather, GPS-denied, and highly dynamic environments for both research and industrial applications. The former include LiDAR data processing in adverse weather, object detection, and autonomous navigation. The latter encompasses autonomous driving, service robotics, and public health crises applications. We hope that our efforts can effectively provide readers with a reference based on our hands-on experiences and promote the deployment of existing mature technologies in real-world systems.
  Citation: Unmanned Systems
  PubDate: 2024-06-22T07:00:00Z
  DOI: 10.1142/S230138502530001X
Realization of Robust Formation for Multi-UAV Systems Using Control
Barrier Functions
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  Authors: Zhaoming Zhang, Yiming Wu, Jie Jiang, Ning Zheng, Wei Meng
  Pages: 1 - 15
  Abstract: Unmanned Systems, Ahead of Print.
  Network robustness is a necessary prerequisite for the effective execution of resilient control in distributed multiple unmanned aerial vehicles (UAVs). However, it remains a challenging task to construct a communication graph that satisfies specific network robustness properties. This paper investigates robust formation control of multi-UAV systems using control barrier functions (CBFs). We first propose a novel formation law to drive groups of UAVs into formations with communication graphs that satisfy the [math]-robustness. With such a law, all the normal UAVs in the formation are capable of executing a given resilient consensus protocol, and achieving convergence in the presence of malicious attackers. Then, we present a control law that facilitates the establishment of UAV formations, in which the communication graphs satisfy p-fraction robustness. Finally, simulation examples are given to verify the effectiveness of the proposed formation control laws.
  Citation: Unmanned Systems
  PubDate: 2024-06-14T07:00:00Z
  DOI: 10.1142/S2301385025500451
Adaptive Impedance Control for Teleoperation with Event-Triggered
Controller
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  Authors: Jiangbo Zhao, Bowen Duan, Junzheng Wang
  Pages: 1 - 15
  Abstract: Unmanned Systems, Ahead of Print.
  Uncertainty in the master–slave model is one of the primary factors affecting the transparency of teleoperation systems, and congestion in the master–slave communication network also greatly influences the performance of the teleoperation system. This paper proposes a combined framework of adaptive and impedance control to address the uncertainty in the master–slave model and achieve smooth operation at the slave end. Building upon this linear model, an event-triggered mechanism is designed using Lyapunov functions, with dynamic online adjustment of the triggering threshold parameters. Following the completion of the aforementioned research, control objectives are established to validate the performance of the teleoperation control system proposed in this paper. Finally, simulation verification is conducted in the Matlab/Simulink environment.
  Citation: Unmanned Systems
  PubDate: 2024-06-12T07:00:00Z
  DOI: 10.1142/S2301385025500402
Global Path Planning Algorithm in a Two-Dimensional Environment with
Polygonal Obstacles on the Class of Piecewise Polygonal Trajectories
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  Authors: Vladimir Kostyukov, Mikhail Medvedev, Viacheslav Pshikhopov
  Pages: 1 - 19
  Abstract: Unmanned Systems, Ahead of Print.
  The paper discusses a motion planner with increased performance in relation to a number of common planning algorithms for maps with obstacles of complex shapes. An algorithm is substantiated for searching for the optimal path in terms of length in the class of piecewise broken curves on a special graph that combines some characteristic points of each obstacle. An estimate of the improved upper bound on the complexity of the algorithm as a function of the number of obstacles is given. Theoretical calculations are confirmed by the results of numerical simulation.
  Citation: Unmanned Systems
  PubDate: 2024-06-05T07:00:00Z
  DOI: 10.1142/S2301385025500438
Sensing and Control Integration for Thrust Vectoring in Heavy UAVs:
Real-World Implementation and Performance Analysis
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  Authors: Mohammad Sadeq Ale Isaac, Pablo Flores Peña, Marco Andrés Luna, Ahmed Refaat Ragab, Pascual Campoy
  Pages: 1 - 23
  Abstract: Unmanned Systems, Ahead of Print.
  Unmanned Aerial Vehicles (UAVs) have garnered significant attention among researchers due to their versatility in diverse missions and resilience in challenging conditions. However, electric UAVs often suffer from limited flight autonomy, necessitating the exploration of alternative power sources such as thermal engines. On the other hand, managing thermal engines introduces complexities and internal uncertainties into the system. In this paper, an Adaptive Robust attitude controller (ARAC) is proposed to address these challenges by drawing inspiration from helicopter solutions while minimizing mechanical intricacies. Specifically, the designed algorithm employs Thrust Vector Control (TVC) for an industrial heavy Multi-Ducted Fan (MDF), known for its superior static stability compared to conventional ducted fans. Subsequently, an integrated flap vanes system is positioned at the exhaust of the ducts for precise attitude control, effectively removing unwanted yaw moments associated with traditional propellers. This research builds on prior authors’ works to establish a proper mathematical and aerodynamic model. Also, using former simulation results to conduct real flight experiments aimed at enhancing TVC functionality. The findings highlight the effectiveness of this approach for heavy UAV applications. It is worth noting that the practical value of this research lies in its potential to significantly extend flight autonomy supplied by thermal engines and improve the resilience of UAVs in challenging real-world missions. This is particularly achievable provided that the design of flap vanes aligns closely with the dimensions of the duct system, offering a promising solution to a critical engineering challenge in the field of UAV technology.
  Citation: Unmanned Systems
  PubDate: 2024-06-04T07:00:00Z
  DOI: 10.1142/S2301385025500396
Foot-End Global Trajectory Planning via GCN-Based Heuristic Tree Search
for Crossing Obstacles
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  Authors: Junfeng Xue, Shoukun Wang, Jinge Si, Junzheng Wang
  Pages: 1 - 12
  Abstract: Unmanned Systems, Ahead of Print.
  Achieving smooth motion for multi-legged robots on complex terrains is a significant focus of research. When encountering high obstacles, robots often need to alter their motion direction to avoid them, increasing redundancy in their motion trajectories. To address this challenge, this paper proposes a method for planning the foot-end trajectory during the swing phase while considering obstacle avoidance without modifying the fuselage trajectory. The method combines the Global Optimal Path Search Tree (GOPST) algorithm and a prior path estimation method utilizing Graph Convolutional Network (GCN). The GOPST explores multiple global paths by conducting local path tree searches in each step, guided by an objective function. To enhance efficiency, redundant search branches with high intensity or high possibility of collision with obstacles or other feet, etc. are eliminated using an event-triggering mechanism based on expert constraints. Another objective function is also formulated to obtain an optimal path that offers a larger safety space and a shorter path length. The optimal path nodes and their environmental features are integrated into a GCN for training. Before the operation of the GOPST, the GCN network provides a preliminary path with fast estimation speed. If the estimated path falls outside the safety margin, the GOPST is reactivated to explore a reliable path. Numerical simulation results validate that the GOPST–GCN approach can rapidly generate a smooth trajectory within the safety space of the foot-end workspace. Furthermore, the search time for finding the optimal path in an untrained environment decreases as the number of tests increases. Experimental verification confirms that robots successfully avoid obstacles by employing foot-end swinging without altering the initial motion direction of the fuselage. The GOPST–GCN algorithm is publicly available at https://github.com/bjmyX/GOPST-GCN-a-foot-end-path-planning-method-.
  Citation: Unmanned Systems
  PubDate: 2024-05-25T07:00:00Z
  DOI: 10.1142/S2301385025500414
A Sampling-Based Approach to Solve Difficult Path Planning Queries
Efficiently in Narrow Environments for Autonomous Ground Vehicles
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  Authors: Domokos Kiss
  Pages: 1 - 26
  Abstract: Unmanned Systems, Ahead of Print.
  Path planning is an essential subproblem of autonomous robots’ navigation. Reaching a given goal pose or covering the available space are typical navigation missions, that require different planning approaches. We focus on such problems in this paper, where a goal pose must be reached by a wheeled autonomous ground vehicle in challenging situations, i.e. in complex environments with limited free space. Many path-planning methods are available, from which the sampling-based approaches gained the highest interest due to their computational efficiency. However, the performance of such methods degrades if the free space is limited and narrow passages have to be crossed on the way to the goal. Finding real-time planning methods to deliver high-quality paths in such situations is challenging. This paper aims to take steps toward solving this problem. On the one hand, an approach is presented to characterize free space narrowness and the difficulty of planning tasks. This can be used as a tool to compare planning queries and evaluate the performance of planning methods from the perspective of their sensitivity to environmental narrowness. On the other hand, an improved variant of our previously proposed RTR planner, an incremental sampling-based path-planning method, is introduced that exhibits good performance even in narrow and difficult planning situations. It is shown by simulations that it outperforms the popular RRT and RRT* planners in terms of running time and path quality, and that it is less sensitive to the narrowness of the environment where the planning task has to be solved.
  Citation: Unmanned Systems
  PubDate: 2024-05-22T07:00:00Z
  DOI: 10.1142/S2301385025500426
Trajectory Planning for Autonomous Formation of Wheeled Mobile Robots via
Modified Artificial Potential Field and Improved PSO Algorithm
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  Authors: Nour el Islem Bouaziz, Omar Mechali, Khadir Lakhdar Besseghieur, Nouara Achour
  Pages: 1 - 20
  Abstract: Unmanned Systems, Ahead of Print.
  This paper addresses two substantial aspects in the field of robotics: trajectory planning and trajectory tracking for multi-robot systems. The collective movement of the multi-wheeled mobile robots is based on a formation control with a leader–follower strategy. To ensure safe navigation toward the destination within a workspace containing multiple obstacles, we skillfully combine the Modified Artificial Potential Field (MAPF) method and the Improved Particle Swarm Optimization (IPSO) algorithm for a group of nonholonomic mobile robots. A global planner based on IPSO algorithm is assigned to the leader robot, to find the optimal collision-free path. Compared to recent nature-inspired methodologies, the improvement suggested enhance the search capabilities of the algorithm, resulting in a 2% reduction in path length and a 29% decrease in computation time. Simultaneously, the follower robot has the ability to employ either a formation policy or a local planner using MAPF. The proposed modifications address the primary limitations of the conventional method, notably the susceptibility to local minima and the challenge of reaching goals near obstacles. Furthermore, this local planner is adept at evading both static and dynamic obstacles. Extensive real hardware experiments are conducted within multiple scenarios using the mobile robot Qbot-2e to corroborate the obtained simulation results and validate the effectiveness of the proposed approaches both in tracking the desired trajectories, finding the optimal feasible collision-free path and avoiding static and dynamic obstacles for multi-robot systems.
  Citation: Unmanned Systems
  PubDate: 2024-05-08T07:00:00Z
  DOI: 10.1142/S2301385025500372
LENet: Lightweight and Effective Detector for Aerial Object
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  Authors: Xunkuai Zhou, Li Li, Ben M. Chen
  Pages: 1 - 17
  Abstract: Unmanned Systems, Ahead of Print.
  Aerial object detection is crucial in various computer vision tasks, including video monitoring, early warning systems, and visual tracking. While current methods can accurately detect normal-sized objects, they face challenges distinguishing small objects from cluttered backgrounds. Developing methods that can be deployed on edge devices to achieve fast, accurate, and energy-efficient performance is also an urgent challenge. This paper proposes a network for aerial object detection by incorporating an attention mechanism to enhance feature extraction and elevate the accuracy of aerial moving object detection. Additionally, we optimize the channel dimensions of the feature extraction framework, resulting in a reduction in model parameters, acceleration of inference speed, and alleviation of the computational burden. Ulteriorly, we optimize the Spatial Pyramid Pooling (SPP) module to enhance detection accuracy and processing speed. Inspired by the ResNet and RepVGG structures, we design a feature fusion module to combine early-extracted features, improving speed and accuracy. Based on the design mentioned above principles, we develop a neural network method with an impressively small model size of only [math][math]M. The proposed approach achieves state-of-the-art performance on five benchmark datasets. Besides its superior performance, our method demonstrates excellent throughput on edge computing devices. Experimental results show that even when running on low-performance computing devices, the CPU and GPU temperatures remain below 50∘C and achieve a detection speed of 14.8 frames per second (fps) and power consumption of only 2.9[math]W. These findings suggest that a high-accuracy, low-power, low-latency, and low-memory footprint aerial object detection solution is achievable.
  Citation: Unmanned Systems
  PubDate: 2024-05-08T07:00:00Z
  DOI: 10.1142/S2301385025500384
Investigating Tailless UAV Flight Dynamics through Modeling, Simulation,
and Flight Testing
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  Authors: Noureldein Ahmed, Mohamed Y. Zakaria, Ashraf M. Kamal
  Pages: 1 - 21
  Abstract: Unmanned Systems, Ahead of Print.
  Tailless Unmanned Aerial Vehicles (UAVs) offer several advantages over their conventional counterparts, including enhanced maneuverability, higher durability, and better stealth. However, they are challenging in their stability and control due to the absence of stabilizing and control surfaces that typically exist in conventional empennage. A crucial process for analyzing the tailless UAVs’ stability/performance characteristics and determining/validating their flight control parameters is done via the modeling and simulation of flight dynamics. This paper presents a comprehensive and systematic procedure for investigating the flight dynamics of a tailless UAV, including modeling, simulation, analytical verification, and flight testing, while also explaining the interconnections among these elements. It also addresses the common challenge of limited accessibility of UAV essential data through using diverse analytical, empirical, and experimental methods. First, a rapid and effective first-principles modeling approach is introduced to simulate the nonlinear six-degree-of-freedom flight dynamics of a small tailless UAV case study. The modeling process follows a modular framework where well-defined experiments and commercial of-the-shelf software, tools, and sensors are employed to build the necessary sub-models, including geometric, mass–inertia, aerodynamic, propulsion, and actuator models. Then, all sub-models are integrated into a simulation environment to allow the prediction of the UAV dynamic response obtained from the given control inputs. The developed flight dynamic model is subjected to a thorough verification process to ensure its integrity and proper functionality by comparing the simulated trim and natural flight modes with the calculated analytical results. Finally, a set of specific flight tests are performed to validate the developed simulation model and verify relevant performance characteristics for the case-study UAV. The results show that the proposed approach provides a systematic and straightforward method for examining the flight dynamics of small tailless UAVs with reasonable accuracy.
  Citation: Unmanned Systems
  PubDate: 2024-04-25T07:00:00Z
  DOI: 10.1142/S230138502550027X
An Input–Output Feedback Linearization-Based Exponentially Stable
Controller for Multi-UAV Payload Transport
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  Authors: Nishanth Rao, Suresh Sundaram
  Pages: 1 - 19
  Abstract: Unmanned Systems, Ahead of Print.
  In this paper, an exponentially stable trajectory tracking controller is proposed for multi-UAV payload transport. The multi-UAV payload system has a 2-DOF magnetic spherical joint between the UAVs and the vertical rigid links of the payload frame so that the UAVs can roll or pitch freely. These vertical links are rigidly attached to the payload and cannot move. An input–output feedback linearized model is derived for the complete payload-UAV system along with thrust vectoring control for trajectory tracking of the payload. Theoretical analysis of tracking control laws is shown to be exponentially stable, thus guaranteeing safe transportation along the desired trajectory. To validate the performance of the proposed control law, the results for a numerical simulation as well as a high-fidelity Gazebo real-time simulation are presented. The tracking performance is compared to the existing techniques in the literature, and it is seen that the proposed approach has the least RMSE for tracking performance. Next, the robustness of the proposed controller is analyzed against two practical situations: External disturbance on the payload and payload mass uncertainty. The results clearly indicate that the proposed controller is robust and computationally efficient while achieving exponentially stable trajectory tracking.
  Citation: Unmanned Systems
  PubDate: 2024-04-25T07:00:00Z
  DOI: 10.1142/S2301385025500311
Multi-Domain Collaborative Task Allocation and Conflict Resolution in
Unmanned Systems under Complex Constraints
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  Authors: Qihui Liu, Kuihua Huang, Haiying Liu, Rui Wang, Guangquan Cheng
  Pages: 1 - 11
  Abstract: Unmanned Systems, Ahead of Print.
  Under the background that multi-domain collaborative tasks with complex constraints executed by heterogeneous unmanned systems, based on a consensus-based bundle algorithm, a consensus-based synergy algorithm is proposed to solve the distributed multi-domain collaborative task planning problem. First, a multi-domain collaborative task allocation model and score evaluation system are established by considering task resource requirement constraints, task timing constraints, and path threat constraints. Second, the time sequence constraints of collaborative tasks are transformed into elastic time window constraints. The bidding method based on collaborative constraints is used for task allocation, and the improved consistency conflict resolution principle is adopted to realize distributed multi-domain collaborative task allocation conflict resolution. Finally, the path planning is coupled to the task allocation process by using the Bezier curve path, and the results of multi-domain collaborative task allocation and path planning are obtained synchronously. Simulation results show that the proposed algorithm can effectively solve the problem of multi-domain collaborative task planning for heterogeneous unmanned systems.
  Citation: Unmanned Systems
  PubDate: 2024-04-25T07:00:00Z
  DOI: 10.1142/S2301385025500347
Innovative Stripe Noise Image Correction Method for Remote Sensing
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  Authors: Sid Ahmed Hamadouche, Ayoub Boutemedjet, Azzedine Bouaraba
  Pages: 1 - 17
  Abstract: Unmanned Systems, Ahead of Print.
  Due to their propensity for stripe noise distortions, infrared remote sensing images present substantial difficulty for interpretation. Our ability to address this issue by offering an easy, efficient, and fast technique for image stripe noise correction is what makes our work unique. Our proposed solution tackles stripe noise by subtracting the mean value along the stripes from the noisy image. Additionally, we leverage the wavelet transform on the average signal to exploit the inherent sparsity of noise in the wavelet domain. This approach not only enhances denoising performance without introducing blurring effects but also enables us to recover image details with remarkable precision, all without the need for intricate algorithms, iterative processes, or training models. To validate the effectiveness of our approach, we conducted evaluations using a dataset of real-world infrared remote sensing images. This dataset encompasses a wide range of examples, featuring both real and artificially induced noise scenarios.
  Citation: Unmanned Systems
  PubDate: 2024-04-03T07:00:00Z
  DOI: 10.1142/S2301385025500335
Optimal Control Approach to Design a Three-Loop Autopilot for a Tactical
Missile
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  Authors: A. M. Abu El-Wafa, Mohammed A. H. Abozied, Yehia Z. Elhalwagy, Hossam M. Hendy
  Pages: 1 - 18
  Abstract: Unmanned Systems, Ahead of Print.
  Conventionally, the Three-Loop Autopilot is dominated by three main parameters namely the damping factor, time constant and the open-loop crossover frequency, to achieve the desired performance. In developing the autopilot gains, the same gains as the conventional approach can also be obtained using the general optimality theory. The basic idea of setting the design parameters and the feedback gains using methods of optimal control revolves around the proper selection of the performance index for which the control is optimized. In this paper, an explicit formula for autopilot gains is driven by taking into account the relations between the desired performance and the open-loop frequency, phase margin and time constant without the need to adjust the LQR weights. Since each set of design parameters selection doesn’t guarantee the existence of the optimal control law, the optimization criteria for the three-loop autopilot are derived to know the set of design parameters for which the control is optimized. Finally, an optimal criterion to select the design parameters and therefore the autopilot gains is developed, where the time constant is set to the designer objective while the open-loop crossover frequency and the phase margin as design constraints.
  Citation: Unmanned Systems
  PubDate: 2024-04-03T07:00:00Z
  DOI: 10.1142/S2301385025500359
Precision Advancements in Aerial Gliding Vehicles: Modeling to FCS
Validation
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  Authors: Mohamed Ibrahim Mohamed, Ehab Safwat, Mohammed A. H. Abozied, Ahmed M. Kamel, Yehia Z. Elhalwagy
  Pages: 1 - 20
  Abstract: Unmanned Systems, Ahead of Print.
  The growing utilization of unmanned aerial vehicles (UAVs) in military operations has necessitated the development of a suitable weaponry for these kind of platforms. One of the trending categories of such armaments is the aerial gliding vehicle (AGV). AGVs have no propulsion system, consequently, a critical need for a robust flight control system (FCS) tailored to this kind of aerial systems is raised. This research focuses on designing a nonlinear model based controller, starting with the construction of a precise model through practical experiments and the establishment of a dedicated testing and flight simulation environment. Recognizing the limitations of traditional nonlinear dynamic inversion (NDI) due to its dependence on the vehicle model, the modified incremental nonlinear dynamic inversion (MI-NDI) is developed to operate in the presence of wind, model mismatches, and external disturbances. In this research, an extensive testing is conducted in a hardware-in-the-loop (HIL) simulation environment which validates the MI-NDI controller’s superior performance, even in challenging conditions. The research outcomes mark a significant advancement in enhancing autopilot precision for advanced aerial weaponry and unmanned vehicles.
  Citation: Unmanned Systems
  PubDate: 2024-04-01T07:00:00Z
  DOI: 10.1142/S2301385025500323
Indoor 2D Autonomous Exploration with an Omnidirectional Robot: A Strategy
Based on Rapidly-Exploring Random Trees
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  Authors: Ecem Sumer, Mehmed Rafi Imamoglu, Hakan Temeltas
  Pages: 1 - 14
  Abstract: Unmanned Systems, Ahead of Print.
  An essential characteristic of a fully autonomous robot is the capability to examine an unfamiliar environment and construct a representation of it. The challenge of autonomous exploration involves overcoming various sub-problems, including Simultaneous Localization and Mapping (SLAM), motion planning, target identification, and informed decision-making for target selection. This paper presents a frontier-based methodology to identify potential navigation targets for the autonomous exploration of unknown environments by an omnidirectional robot. Permanent and temporary Rapidly-exploring Random Tree (RRT)-based structures are used to search the map and detect frontier points. A novel temporary RRT-based structure, Frontier Temporary Tree, is introduced in this study. It is noteworthy that RRT is solely used to search the explorable space for frontier points and does not contribute to motion planning. A cost-benefit analysis, taking into account path cost, heading cost, and information gain, is used to evaluate the frontier points and determine the best target among them. The proposed method is subjected to rigorous testing through both simulation and experimental studies with an omnidirectional robot under real-world scenarios. Comparative results from simulation studies show that our method consistently outperforms, demonstrating its robustness and efficacy.
  Citation: Unmanned Systems
  PubDate: 2024-03-15T07:00:00Z
  DOI: 10.1142/S2301385025500281
Real-Time UAV Trajectory Prediction for UTM Surveillance Using Machine
Learning
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  Authors: Neno Ruseno, Chung-Yan Lin
  Pages: 1 - 15
  Abstract: Unmanned Systems, Ahead of Print.
  The Unmanned Aerial System (UAS) Traffic Management (UTM) surveillance plays an important role in monitoring the safety compliance of UAV flights within specific operational zones. However, the quality of data reception from UAVs depends on transmission signal quality, leading to variable data latency during flights. As a means of addressing this issue and improving UAV operational safety, trajectory prediction emerges as a promising solution. This study aims to implement real-time trajectory prediction through the integration of machine learning techniques within the UTM surveillance system using Broadcast Remote ID. To facilitate this, a homemade receiver for broadcast Remote ID is developed using the ESP32 microcontroller. Subsequently, two distinctive flight tests are executed using a DJI Phantom 4 UAV. In the first flight, UAV trajectory data are used to serve as training input for the machine learning algorithms. The second flight focuses on the implementation of real-time trajectory prediction. The trajectory prediction model uses inputs such as latitude, longitude, height, speed, direction, latency time, and the Received Signal Strength Indicator (RSSI). Through an analysis of the first flight’s offline data, the Gated Recurrent Unit (GRU) algorithm is preferred to the Long Short-Term Memory (LSTM) algorithm. The outcomes of the second flight test prove the feasibility of the GRU-based trajectory prediction. The GRU model successfully produces real-time predictions during UAV flight, showcasing accuracy levels similar to those derived from offline prediction analyses with short processing time. This real-time trajectory prediction could improve the safety of UAV operation by providing the estimated position when the latency time is higher than the threshold.
  Citation: Unmanned Systems
  PubDate: 2024-03-14T07:00:00Z
  DOI: 10.1142/S230138502550030X
LiDAR Depth Cluster Active Detection and Localization for a UAV with
Partial Information Loss in GNSS
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  Authors: Chencheng Deng, Shoukun Wang, Junzheng Wang, Yongkang Xu, Zhihua Chen
  Pages: 1 - 13
  Abstract: Unmanned Systems, Ahead of Print.
  Accurate and robust state estimation is critical for the heterogeneous agent systems, particularly when considering the challenges posed by Unmanned Aerial Vehicles (UAVs) operating in perceptually-degraded environments where access to Global Navigation Satellite System (GNSS) signals is lost. We can, however, actively increase the amount of optimal localization available to UAV by augmenting them with a small number of more expensive, but less resource-constrained, heterogeneous agents. In this paper, we propose a novel detection, localization, and tracking framework for UAV based on LiDAR. First, we present an innovative approach that integrates range image projection and Depth Cluster of LiDAR point clouds with UAV technology. Subsequently, we devise a multidimensional feature probability detection and tracking evaluation function, enabling the detection, estimation, and active tracking of UAV movement. Finally, we conduct comprehensive experiments using heterogeneous agent systems to assess the effectiveness and robustness of the developed framework. The experiments reveal a minimum 20% reduction in running time and an average localization accuracy error of 1.98[math]cm.
  Citation: Unmanned Systems
  PubDate: 2024-03-13T07:00:00Z
  DOI: 10.1142/S2301385025500293
Robust Control of Golf Swing Robot Using Backstepping Based on Fuzzy
Sliding-Mode and Super-Twisting Backstepping Sliding-Mode Algorithms
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  Authors: Souhila Benmansour, Fethi Demim, Ali Zakaria Messaoui
  Pages: 1 - 15
  Abstract: Unmanned Systems, Ahead of Print.
  This paper focuses on trajectory tracking, robustness and stabilization of a golf swing robot which has been recently developed to simulate the ultra-high-speed swing motions of a golfer. The proposed control strategies are based on the Lyapunov stability theory and include Backstepping and Sliding-Mode Control based techniques. To attenuate the chattering phenomena caused by a discontinuous switching function and improve the dynamic response of the manipulator, a fuzzy system is used in this research; a Backstepping Sliding-Mode Controller (BSMC), a Backstepping Fuzzy Sliding-Mode Controller (BFSMC) and a Super-twisting Backstepping Sliding-Mode Controller (STBSMC) are used to evaluate the proposed hybrid controller’s BFSMC performance. The Lyapunov stability theory is used to guarantee the stability of the proposed closed-loop robot technique. Numerical simulations show the effectiveness of the proposed strategy based on the fuzzy logic mechanism under different disturbances and uncertainties.
  Citation: Unmanned Systems
  PubDate: 2024-03-08T08:00:00Z
  DOI: 10.1142/S2301385025500268
A New Self-guidance Approach in UAV for Emergency Surveillance
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  Authors: Vipin Kumar Pandey, Suddhasil De
  Pages: 1 - 12
  Abstract: Unmanned Systems, Ahead of Print.
  With advances in unmanned aerial vehicle (UAV) technologies, they have been put to use in a wide variety of scenarios, including emergency surveillance and disaster management. However, their widespread usage is still limited due to their expensive nature. Only user-piloted UAVs are extensively used still today; but their low-budget design leads to lack of auto-guidance and cooperative functions in both line-of-sight and obstacle-sensitive operations. This paper presents a new approach of self-guidance in these budget UAVs for stable maneuverability even in the obstacle-sensitive surroundings. Further, the paper additionally incorporates cooperative functioning in such self-guided budget UAVs for completing collaborative tasks of emergency surveillance. Comparative testbed results show the cost-effective benefits of the proposed methods and the precise working abilities in no-obstacle as well as obstacle-sensitive sites.
  Citation: Unmanned Systems
  PubDate: 2024-02-29T08:00:00Z
  DOI: 10.1142/S2301385025500256
A Greedy-Strategy-Based Iterative Optimization Method for Articulated
Vehicle Global Trajectory Optimization in Complex Environments
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  Authors: Bikang Hua, Runqi Chai, Kaiyuan Chen, Hankun Jiang, Senchun Chai, Yuanqing Xia
  Pages: 1 - 14
  Abstract: Unmanned Systems, Ahead of Print.
  This paper considers the problem of trajectory planning for articulated vehicles in complex environments. We formulate this problem as an optimal control problem (OCP) and propose a greedy-strategy-based planner. This planner consists of three stages. In stage 1, an IAA* algorithm is proposed to identify the homotopy class. In stage 2, the collision-free tunnels are constructed along the guiding trajectory generated in stage 1 to simplify the intractable collision-avoidance constraints. In stage 3, a greedy-strategy-based iterative optimization (GSIO) framework is designed, which contributes to escaping from local optimums, making the optimization process more targeted, and converging to the global optimum solution quickly, especially in complex tasks. One feature of the proposed planner is that it is suitable for any type of articulated vehicle, and the proposed optimization framework can be used as an open framework to optimize any criterion that can be described explicitly by a polynomial. Furthermore, in the set simulation cases, our work shows significant competitiveness, under the premise of ensuring moderate CPU processing time, our algorithm achieves approximately a 40% performance improvement in optimization effects compared to selected comparative algorithms.
  Citation: Unmanned Systems
  PubDate: 2024-02-05T08:00:00Z
  DOI: 10.1142/S2301385025500244
Development and Flight Testing of Guidance, Navigation, Control, and
Operator Interface for Shipboard Helicopter Operations
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  Authors: Jintasit Pravitra, Eric N. Johnson
  Pages: 1 - 16
  Abstract: Unmanned Systems, Ahead of Print.
  Unmanned Aircraft Systems (UASs) have become increasingly important assets for naval and maritime law enforcement operations. In response to the ever-increasing demand for autonomous systems, this paper presents a design, development, integration, and flight testing of an unmanned rotorcraft system capable of operating with a moving ship. The system was developed initially by playing back ship motion data in a simulation environment. Ship motion prediction, relative guidance, operator control interface, and landing flight management system were developed and tested against simulated sea state motion. The system was then flight tested using a 15-kg electric helicopter operating off a US Naval Academy’s yard patrol craft. The craft was instrumented with IMU and GPS for the craft’s own state estimation. The craft was also used as a moving-base station for Real-time Kinematic (RTK) relative positioning. The flight operation was done in the Chesapeake Bay. Autonomous capabilities including takeoff, landing, station-keeping, and maneuvering relative to a moving ship were successfully demonstrated.
  Citation: Unmanned Systems
  PubDate: 2024-02-03T08:00:00Z
  DOI: 10.1142/S2301385025500220
A Novel Battery Management and Recovery Control Algorithm for Mitigating
the Memory Effect in Nickel Hydrogen Batteries for Space Missions
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  Authors: Ahmed Mokhtar Mohamed, Fawzy ElTohamy H. Amer, Yehia Z. Elhalwagy, Mohamed E. Hanafy
  Pages: 1 - 20
  Abstract: Unmanned Systems, Ahead of Print.
  The spacecraft’s (SC) electric power system (EPS) is one of the vital systems. It could limit the SC capabilities or reduce the SC lifetime. The EPS should provide the required energy for the SC loads to perform their tasks successfully until End-of-Life (EOL). Furthermore, the EPS should guarantee the power needed to execute the customer requirements under all degradations (battery degradation, radiation, etc.) and other external factors (orbital degradation). The SC’s primary power generation sources are solar arrays and storage batteries. This paper investigates a SC with a Nickel Hydrogen Storage Battery (NHSB) consisting of 17 cells: 4 measured cells and 13 unmeasured cells. In this paper, two distinct objectives have been achieved: The first objective is formulating a control algorithm for battery testing and reconditioning (CABTR). This algorithm is applied to an NHSB in an orbital setting mode, rectifying the memory effect in the NHSB and restoring the battery’s total capacity. The second objective constructs an environment that effectively simulates the dynamic behavior of the battery during in-orbit operations. This simulated environment calculates crucial parameters such as the state of charging (SOC), battery temperature, voltage, and pressure of the NHSB during reconditioning. This computational analysis assures the robustness of the CABTR, and confirms the optimal battery performance. Subsequent validation encompasses ground tests and real-time telemetry data, which affirms the battery’s dynamic behavior and the developed algorithms’ efficacy.
  Citation: Unmanned Systems
  PubDate: 2024-02-01T08:00:00Z
  DOI: 10.1142/S2301385025500219
UAV Trajectory Optimization for Maximum Soaring in Windy Environment
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  Authors: Hassan Haghighi, Daniel Delahaye, Jean-Marc Moschetta, Davood asadi
  Pages: 1 - 14
  Abstract: Unmanned Systems, Ahead of Print.
  Optimal trajectory planning in a windy environment is a complex problem when airplane performance characteristics are considered. This paper introduces a novel form of Legendre pseudospectral optimization to solve boundary value problems in UAV trajectory planning. The proposed architecture applies Legendre–Gauss–Lobatto and Hamilton–Jacobi–Bellman equations to generate candidate pieces of trajectories with respect to the UAV dynamic constraints. Analytical performance-based solutions are also developed for sample cases to achieve an optimal criterion in trajectory planning. Moreover, the notion of wind soaring is exploited to use the beneficial effects of tailwind velocities in trajectory planning. Integral cost functions are handled by Gauss-type quadrature rules. Simulations demonstrate the efficiency of the pseudospectral method compared with other solvers, in eliminating the difficulties of boundary value problems by employing the boundary points in the interpolation equation. The effectiveness of the proposed approach is demonstrated through dynamic simulations.
  Citation: Unmanned Systems
  PubDate: 2024-01-12T08:00:00Z
  DOI: 10.1142/S2301385025500232

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