Search Results (278)

Aiming at the cooperative path-planning problem of multiple autonomous underwater vehicles in underwater three-dimensional terrain and dynamic ocean current environments, a hybrid algorithm based on the Improved Multi-Objective Particle Swarm Optimization (IMOPSO) and Dynamic Window (DWA) is proposed. The traditional particle swarm optimization algorithm is prone to falling into local optimization in high-dimensional and complex marine environments. It is difficult to meet multiple constraint conditions, the particle distribution is uneven, and the adaptability to dynamic environments is poor. In response to these problems, a hybrid initialization method based on Chebyshev chaotic mapping, pre-iterative elimination, and boundary particle injection (CPB) is proposed, and the particle swarm optimization algorithm is improved by combining dynamic parameter adjustment and a hybrid perturbation mechanism. On this basis, the Dynamic Window Method (DWA) is introduced as the local path optimization module to achieve real-time avoidance of dynamic obstacles and rolling path correction, thereby constructing a globally and locally coupled hybrid path-planning framework. Finally, cubic spline interpolation is used to smooth the planned path. Considering factors such as path length, smoothness, deflection Angle, and ocean current kinetic energy loss, the dynamic penalty function is adopted to optimize the multi-AUV cooperative collision avoidance and terrain constraints. The simulation results show that the proposed algorithm can effectively plan the dynamic safe path planning of multiple AUVs. By comparing it with other algorithms, the efficiency and security of the proposed algorithm are verified, meeting the navigation requirements in the current environment. Experiments show that the IMOPSO–DWA hybrid algorithm reduces the path length by 15.5%, the threat penalty by 8.3%, and the total fitness by 3.2% compared with the traditional PSO algorithm. Full article

A great ellipse route (GER), as one of the fundamental routes for ocean voyages, directly influences the actual voyage distance and the complexity of vessel maneuvering through the location and number of its waypoints. Against the backdrop of global warming, the melting of Arctic sea ice has accelerated the opening of the Arctic shipping route. This paper addresses the issue of how to reasonably segment and adopt rhumb line routes to approximate the GER in the special navigational environment of the Arctic. Using historical routes, recommended routes, and geospatial data that have passed through the Arctic shipping lane as constraints, this paper proposes a waypoint optimization model based on an adaptive hybrid particle swarm optimization-genetic algorithm (AHPSOGA). Additionally, by integrating Arctic remote sensing ice condition data and the Polar Operational Limit Assessment Risk Indexing System (POLARIS), a safety assessment model tailored for this route has been developed, enabling the quantification of sea ice risks and dynamic evaluation of segment safety. Experimental results indicate that the proposed waypoint optimization model reduces the number of waypoints and voyage distance compared to recommended routes and conventional shipping industry methods. Furthermore, the AHPSOGA algorithm achieves a 16.41% and 19.19% improvement in convergence speed compared to traditional GA and PSO algorithms, respectively. In terms of computational efficiency, the average runtime is improved by approximately 12.00% and 14.53%, respectively. The risk levels of each segment of the optimized route are comparable to those of the recommended Northeast Passage route. This study provides an effective theoretical foundation and technical support for intelligent planning and decision-making for Arctic shipping routes. Full article

Aiming at the complex construction environment and autonomous navigation challenges in underground coal mine roadways, this paper proposes a path navigation and deviation correction control method for tracked roadheaders in confined roadway spaces. First, a two-dimensional planar grid model of the working scenario was constructed, with dimensionality reduction in the roadway model achieved through a heading reference influence degree threshold of the tracked roadheaders. Based on the kinematics theory of tracked roadheaders, kinematic and dynamic models for deviation correction in fully mechanized excavation roadways were established. Subsequently, a path planning and tracking correction algorithm was developed, along with a heading deviation correction control algorithm based on fuzzy neural network PID. Online optimization of the particle swarm algorithm was realized through crossover-mutation operations, enabling optimal strategy solving for construction path planning and precise control of travel deviation correction. Finally, simulation experiments evaluating algorithm performance and comparative simulations of control algorithms validated the feasibility and superiority of the proposed method. This research provides strategic guidance and theoretical foundations for rapid precision deployment and intelligent deviation correction control of tracked engineering vehicles in confined underground coal mine spaces. Full article

This work formulates and solves the actual problem of studying the logistics of unmanned aerial vehicle (UAV) operations in facility security planning. The study is related to security tasks, including perimeter control, infrastructure condition monitoring, prevention of unauthorized access, and analysis of potential threats. Thus, the topic of the proposed publication is relevant as it examines the sequence of logistical actions in the large-scale application of a swarm of drones for facility protection. The purpose of the research is to create a set of mathematical and simulation models that can be used to analyze the capabilities of a drone swarm when organizing security measures. The article analyzes modern problems of using a drone swarm: formation of the swarm, assessment of its potential capabilities, organization of patrols, development of monitoring scenarios, planning of drone routes and assessment of the effectiveness of the security system. Special attention is paid to the possibilities of wave patrols to provide continuous surveillance of the object. In order to form a drone swarm and possibly divide it into groups sent to different surveillance zones, the necessary UAV capacity to effectively perform security tasks is assessed. Possible security scenarios using drone waves are developed as follows: single patrolling with limited resources; two-wave patrolling; and multi-stage patrolling for complete coverage of the protected area with the required number of UAVs. To select priority monitoring areas, the functional potential of drones and current risks are taken into account. An optimization model of rational distribution of drones into groups to ensure effective control of the protected area is created. Possible variants of drone group formation are analyzed as follows: allocation of one priority surveillance zone, formation of a set of key zones, or even distribution of swarm resources along the entire perimeter. Possible scenarios for dividing the drone swarm in flight are developed as follows: dividing the swarm into groups at the launch stage, dividing the swarm at a given navigation point on the route, and repeatedly dividing the swarm at different patrol points. An original algorithm for the formation of drone flight routes for object surveillance based on the simulation modeling of the movement of virtual objects simulating drones has been developed. An agent-based model on the AnyLogic platform was created to study the logistics of security operations. The scientific novelty of the study is related to the actual task of forming possible strategies for using a swarm of drones to provide integrated security of objects, which contributes to improving the efficiency of security and monitoring systems. The results of the study can be used by specialists in security, logistics, infrastructure monitoring and other areas related to the use of drone swarms for effective control and protection of facilities. Full article

This study presented a comprehensive investigation into the performance of object detection models tailored for edge devices, particularly in the context of Unmanned Aerial Vehicle swarms. Object detection plays a pivotal role in enhancing autonomous navigation, situational awareness, and target tracking capabilities within UAV swarms, where computing resources are constrained by the onboard low-cost computers. Initially, a thorough review of the existing literature was conducted to identify state-of-the-art object detection models suitable for deployment on edge devices. These models are evaluated based on their speed, accuracy, and efficiency, with a focus on real-time inference capabilities crucial for Unmanned Aerial Vehicle applications. Following the literature review, selected models undergo empirical validation through custom training using the Vision Meets Drone dataset, a widely recognized dataset for Unmanned Aerial Vehicle-based object detection tasks. Performance metrics such as mean average precision, inference speed, and resource utilization were measured and compared across different models. Lastly, the study extended its analysis beyond traditional object detection to explore the efficacy of instance segmentation and proposed an optimization to an object tracking technique within the context of unmanned Aerial Vehicles. Instance segmentation offers finer-grained object delineation, enabling more precise target or landmark identification and tracking, albeit at higher resource usage and higher inference time. Full article

This study presents a breakthrough in sustainable injection molding by uniquely combining a backpropagation neural network (BPNN) with particle swarm optimization (PSO) to overcome traditional optimization challenges. The BPNN’s exceptional ability to learn complex nonlinear relationships between six key process parameters (including melt temperature and holding pressure) and product quality is amplified by PSO’s intelligent search capability, which efficiently navigates the high-dimensional parameter space. Together, this hybrid approach achieves what neither method could accomplish alone: the BPNN accurately models the intricate process-quality relationships, while PSO rapidly converges on optimal parameter sets that simultaneously meet strict quality targets (66–70 g weight, 3–5 mm thickness) and minimize energy consumption. The significance of this integration is demonstrated through three key outcomes: First, the BPNN-PSO combination reduced optimization time by 40% compared to traditional trial-and-error methods. Second, it achieved remarkable prediction accuracy (RMSE 0.8229 for thickness, 1.5123 for weight) that surpassed standalone BPNN implementations. Third, the method’s efficiency enabled SMEs to achieve CAE-level precision without expensive software, reducing setup costs by approximately 25%. Experimental validation confirmed that the optimized parameters decreased energy use by 28% and material waste by 35% while consistently producing parts within specifications. This research provides manufacturers with a practical, scalable solution that transforms injection molding from an experience-dependent craft to a data-driven science. The BPNN-PSO framework not only delivers superior technical results but does so in a way that is accessible to resource-constrained manufacturers, marking a significant step toward sustainable, intelligent production systems. For SMEs, this framework offers a practical pathway to achieve both economic and environmental sustainability, reducing reliance on resource-intensive CAE tools while cutting production costs by an estimated 22% through waste and energy savings. The study provides a replicable blueprint for implementing data-driven sustainability in injection molding operations without compromising product quality or operational efficiency. Full article

On 10 May 2024, a super geomagnetic storm with a minimum Dst index of less than −400 nT occurred. It has attracted a significant amount of attention in the literature. Using total electron content (TEC) observations from a global navigation satellite system (GNSS), in situ electron density data from the Swarm satellite, and corresponding simulations from the thermosphere–ionosphere–electrodynamics general circulation model (TIEGCM), the dynamic poleward shift of the equatorial ionization anomaly (EIA) during the main phase of the super geomagnetic storm has been explored. The results show that the EIA crests moved poleward from ±15° magnetic latitude (MLat) to ±20° MLat at around 19.6 UT, to ±25° MLat at 21.2 UT, and to ±31° MLat at 22.7 UT. This poleward shift was primarily driven by the enhanced eastward electric field, neutral winds, and ambipolar diffusion. Storm-induced meridional winds can move ionospheric plasma upward/downward along geomagnetic field lines, causing the poleward movement of EIA crests, with minor contributions from zonal winds. Ambipolar diffusion contributes/prevents the formation of EIA crests at most EIA latitudes/the equatorward edge. Full article

To address the challenges of complex low-altitude flight environments for UAVs, where numerous obstacles often lead to GPS signal obstruction and multipath effects, this study proposes an integrated inertial navigation and GPS positioning approach for coordinated cross-path planning in drone swarms. The methodology involves the following: (1) discretizing continuous 3D airspace into grid cells using occupancy grid mapping to construct an environmental model; (2) analyzing dynamic flight characteristics through attitude angle variations in a 3D Cartesian coordinate system; and (3) implementing collaborative state updates and global positioning through fused inertial–GPS navigation. By incorporating Cramér–Rao lower bound optimization, the system achieves effective cross-path planning for drone formations. Experimental results demonstrate a 98.35% mission success rate with inter-drone navigation time differences maintained below 0.5 s, confirming the method’s effectiveness in enabling synchronized swarm operations while maintaining safe distances during cooperative monitoring and low-altitude flight missions. This approach demonstrates significant advantages in coordinated cross-path planning for UAV clusters. Full article

The integration of reconfigurable intelligent surfaces (RISs) with non-terrestrial networks (NTNs), particularly those enabled by unmanned aerial vehicles (UAVs) or drone-based platforms, has emerged as a transformative approach to enhance 6G connectivity in high-mobility scenarios. UAV-assisted NTNs offer flexible deployment, dynamic altitude control, and rapid network reconfiguration, making them ideal candidates for RIS-based signal optimization. However, the high mobility of UAVs and their three-dimensional trajectory dynamics introduce unique challenges in maintaining robust, low-latency links and seamless handovers. This paper presents a comprehensive performance analysis of RIS-assisted UAV-based NTNs, focusing on optimizing RIS phase shifts to maximize the signal-to-interference-plus-noise ratio (SINR), throughput, energy efficiency, and reliability under UAV mobility constraints. A joint optimization framework is proposed that accounts for UAV path loss, aerial shadowing, interference, and user mobility patterns, tailored specifically for aerial communication networks. Extensive simulations are conducted across various UAV operation scenarios, including urban air corridors, rural surveillance routes, drone swarms, emergency response, and aerial delivery systems. The results reveal that RIS deployment significantly enhances the SINR and throughput while navigating energy and latency trade-offs in real time. These findings offer vital insights for deploying RIS-enhanced aerial networks in 6G, supporting mission-critical drone applications and next-generation autonomous systems. Full article

This paper tackles the challenge of achieving robust and precise control for a novel quadrotor featuring continuously variable arm lengths (15 cm to 19 cm), enabling enhanced adaptability in complex environments. Unlike conventional fixed-geometry or discretely morphing unmanned aerial vehicles, this design’s continuous structural changes introduce significant complexities in modeling its time-varying moment of inertia. To address this, we propose a control strategy that decouples dynamic motion from the evolving geometry, allowing for the development of a robust control model. A sliding mode control algorithm, optimized using particle swarm optimization, is implemented to ensure stability and high performance in the presence of uncertainties and noise. Extensive MATLAB 2016 simulations validate the proposed approach, demonstrating superior tracking accuracy in both fixed and variable arm-length configurations, achieving root mean square error values of 0.05 m (fixed arms), 0.06 m (variable arms, path 1), and 0.03 m (variable arms, path 2). Notably, the PSO-tuned SMC controller reduces tracking error by 30% (0.07 m vs. 0.10 m for PID) and achieves a 40% faster settling time during structural transitions. This improvement is attributed to the PSO-optimized SMC parameters that effectively adapt to the continuously changing inertia, concurrently minimizing chattering by 10%. This research advances the field of morphing UAVs by integrating continuous geometric adaptability with precise and robust control, offering significant potential for energy-efficient flight and navigation in confined spaces, as well as applications in autonomous navigation and industrial inspection. Full article

In complex underwater environments, the stability of navigation for autonomous underwater vehicles (AUVs) is critical for mission success. To enhance the reliability of the AUV-integrated navigation system, fault detection technology was investigated. Initially, the causes and classifications of faults within the integrated navigation system were analyzed in detail, and these faults were categorized as either abrupt or gradual, based on variations in sensor output characteristics under fault conditions. To overcome the limitations of the residual chi-square method in detecting gradual faults, a cumulative residual detection approach with error coefficient amplification was proposed. The algorithm enhances gradual fault detection by using eigenvalue analysis and constructing fault-frequency-based error amplification coefficients with non-parametric techniques. Furthermore, to improve the detection of gradual faults, artificial intelligence-based fault detection methods were also explored. Specifically, the particle swarm optimization (PSO) algorithm was employed to optimize the hyperparameters of a long short-term memory (LSTM) neural network, leading to the development of a PSO-LSTM fault detection model. In this model, the fault detection function was formulated by comparing the predictions generated by the PSO-LSTM model with those derived from the Kalman filter. The experimental results demonstrated that the fault detection function formulated by PSO-LSTM exhibited enhanced sensitivity to gradual faults and enabled the timely isolation of faulty sensors. In unfamiliar marine regions, the PSO-LSTM method demonstrates greater stability and avoids the need to recalibrate detection thresholds for each sea area—an important advantage for AUV autonomous navigation in complex environments. Full article

Automation in agricultural machinery, underpinned by the integration of advanced technologies, is revolutionizing sustainable farming practices. Key enabling technologies include multi-source positioning fusion (e.g., RTK-GNSS/LiDAR), intelligent perception systems utilizing multispectral imaging and deep learning algorithms, adaptive control through modular robotic systems and bio-inspired algorithms, and AI-driven data analytics for resource optimization. These technological advancements manifest in significant applications: autonomous field machinery achieving lateral navigation errors below 6 cm, UAVs enabling targeted agrochemical application, reducing pesticide usage by 40%, and smart greenhouses regulating microclimates with ±0.1 °C precision. Collectively, these innovations enhance productivity, optimize resource utilization (water, fertilizers, energy), and mitigate critical labor shortages. However, persistent challenges include technological heterogeneity across diverse agricultural environments, high implementation costs, limitations in adaptability to dynamic field conditions, and adoption barriers, particularly in developing regions. Future progress necessitates prioritizing the development of lightweight edge computing solutions, multi-energy complementary systems (integrating solar, wind, hydropower), distributed collaborative control frameworks, and AI-optimized swarm operations. To democratize these technologies globally, this review synthesizes the evolution of technology and interdisciplinary synergies, concluding with prioritized strategies for advancing agricultural intelligence to align with the Sustainable Development Goals (SDGs) for zero hunger and responsible production. Full article

Path planning in multi-agent UAV swarms is a crucial issue that involves avoiding collisions in dynamic, obstacle-filled environments while consuming the least amount of time and energy possible. This work comprehensively evaluates reinforcement learning (RL) algorithms for multi-agent UAV path planning in 2D and 3D simulated environments. First, we develop a 2D simulation setup using Python in which UAVs (quadcopters), represented as points in space, navigate toward their respective targets while avoiding static obstacles and inter-agent collisions. In the second phase, we transition this comparison to a physics-based 3D simulation, incorporating realistic UAV (fixed wing) dynamics and checkpoint-based navigation. We compared five algorithms, namely, Proximal Policy Optimization (PPO), Soft Actor–Critic (SAC), Deep Deterministic Policy Gradient (DDPG), Trust Region Policy Optimization (TRPO), and Multi–Agent DDPG (MADDPG), in various scenarios. Our findings reveal significant performance differences between the algorithms across multiple dimensions. DDPG consistently demonstrated superior reward optimization and collision avoidance performance, while PPO and MADDPG excelled in the execution time required to reach the goal. Furthermore, our findings reveal how algorithms perform while transitioning from a simplistic 2D setup to a realistic 3D physics-based environment, which is essential for performing sim-to-real transfer. This work provides valuable insights into the suitability of several reinforcement learning (RL) algorithms for developing autonomous systems and UAV swarm navigation. Full article

This research focuses on route optimization for autonomous ground vehicles, with key applications in precision agriculture, logistics and surveillance. Its goal is to create planning techniques that increase productivity and flexibility in changing settings. To achieve this, a PRISMA-based systematic literature review was carried out, encompassing works published during the last five years in databases like IEEE Xplore, ScienceDirect and Scopus. The search focused on topics related to route optimization, unmanned ground vehicles and heuristic algorithms. From the analysis of 56 selected articles, trends, technologies and challenges in real-time route planning were identified. Fifty-seven percent of the recent studies focus on UGV optimization, with prominent applications in agriculture, aiming to maximize efficiency and reduce costs. Heuristic algorithms, such as Humpback Whale Optimization, Firefly Search and Particle Swarm Optimization, are commonly employed to solve complex search problems. The findings underscore the need for more flexible planning techniques that integrate spatiotemporal and curvature constraints, allowing systems to respond effectively to unforeseen changes. By increasing their effectiveness and adaptability in practical situations, our research helps to provide more reliable autonomous navigation solutions for crucial applications. Full article

Accurate self-localization and mutual state estimation are essential for autonomous aerial swarm operations in cooperative exploration, target tracking, and search-and-rescue missions. However, achieving reliable formation positioning in GNSS-denied environments remains a significant challenge. This paper proposes a UAV formation positioning system that integrates inertial navigation with data link-based relative measurements to improve positioning accuracy. Each UAV independently estimates its flight state in real time using onboard IMU data through an inertial navigation fusion method. The estimated states are then transmitted to other UAVs in the formation via a data link, which also provides relative position measurements. Upon receiving data link information, each UAV filters erroneous measurements, time aligns them with its state estimates, and constructs a relative pose optimization factor graph for real-time state estimation. Furthermore, a data selection strategy and a sliding window algorithm are implemented to control data accumulation and mitigate inertial navigation drift. The proposed method is validated through both simulations and real-world two-UAV formation flight experiments. The experimental results demonstrate that the system achieves a $76\%$ reduction in positioning error compared to using data link measurements alone. This approach provides a robust and reliable solution for maintaining precise relative positioning in formation flight without reliance on GNSS. Full article