Search Results (847)

With the rapid development of e-commerce, logistics UAVs (unmanned aerial vehicles) have shown great potential in the field of urban logistics. However, the large-scale operation of logistics UAVs has brought challenges to air traffic management, and the competitiveness of UAV logistics is still weak compared with traditional ground logistics. Therefore, this paper constructs a double-layer route network structure that separates logistics transshipment from terminal delivery. In the delivery layer, a door-to-door distribution mode is adopted, and the transshipment node service location model is constructed, so as to obtain the location of the transshipment node and the service relationship. In the transshipment layer, the index of the route betweenness standard deviation (BSD) is introduced to construct the route network planning model. In order to solve the above model, a double-layer algorithm was designed. In the upper layer, the multi-objective simulated annealing algorithm (MOSA) is used to solve the transshipment node service location issue, and in the lower layer, the multi-objective non-dominated sorting genetic algorithm II (NSGA-II) is adopted to solve the network planning model. Based on real obstacle data and the demand situation, the double-layer network was constructed through simulation experiments. To verify the network rationality, actual flights were carried out on some routes, and it was found that the gap between the UAV’s autonomous flight route time and the theoretical calculations was relatively small. The simulation results show that compared with the single-layer network, the total distance with the double-layer network was reduced by 62.5% and the structural intersection was reduced by 96.9%. Compared with the minimum spanning tree (MST) algorithm, the total task flight distance obtained with the NSGA-II was reduced by 42.4%. The BSD factors can mitigate potential congestion risks. The route network proposed in this paper can effectively reduce the number of intersections and make the UAV passing volume more balanced. Full article

To address the problem of UAV path planning in complex mountainous terrains, this paper comprehensively considers constraints such as natural mountain and obstacle collision threats, the shortest path, and flight altitude. We propose a more practical UAV path planning model that better reflects the actual UAV path planning situation in complex mountainous areas. In order to solve this model, this paper improves the traditional dung beetle optimization (DBO) algorithm and proposes an improved dung beetle optimization (IDBO) algorithm. The IDBO algorithm optimizes the population initialization method based on the concept of symmetry, ensuring that the population is more evenly distributed within the solution space. Additionally, the algorithm introduces a sine–cosine function-based movement strategy, inspired by the symmetry principle, to enhance the search efficiency of individual population members. Furthermore, a population evolution strategy is incorporated to prevent the algorithm from getting stuck in local optima. To demonstrate the algorithm’s performance, tests were conducted using 23 commonly used benchmark functions provided by the CEC 2005 competition and six commonly used engineering problem models provided by the CEC 2020 competition. The results indicate that IDBO significantly outperforms DBO in terms of convergence performance, effectively solving various engineering optimization problems. Finally, experimental tests under three different threat scenarios show that the proposed IDBO algorithm has scientific validity when applied to UAV path planning. This solution method effectively reduces UAV flight energy consumption costs and obstacle collision threats while improving the efficiency and accuracy of UAV path planning. Full article

With the wide application of UAVs in various industries, solving the complex multi-UAV multi-target problem becomes crucial. The assignment and task planning of multi-UAV and multi-target usually need to consider two scenarios. First, before the UAV executes the task, the number and location of the target points need to be determined. It is equivalent to matching UAVs in a situation where the need is determined. Second, in the process of UAV flight, it is necessary to take into account the existing range of the UAV, the number and position of the changed mission points and carry out real-time UAV mission planning. This paper presents a multi-UAV multi-target collaborative task planning algorithm that takes into account these two scenarios. An integer programming algorithm is used to assign target points, and the constraint condition is the shortest range of UAV. The ant colony algorithm is used to plan the path of a single UAV. In this paper, the UAV delivery of disaster relief materials is taken as an example to carry out mathematical modeling and calculate the algorithm. The simulation process starts from the initial location of the UAV at the airport. After a period of flight, the UAV’s voyage information and target location information are updated to carry out real-time mission planning for the UAV. The maximum range of a single UAV is set at 30,000. The simulation results show that the total path length of four UAVs in pre-mission planning is 70,006.49, and the longest path of a single UAVs is 20645.15. In real-time mission planning, the total path length of four UAVs is 43,633.44, and the longest path of a single UAVs is 14,413.56. Over the course of the entire mission, the total path length of the four UAVs is 54,504.00, and the longest path of a single UAV is 16,434.74. The simulation results show that the solution method designed in this paper is efficient and can realize the real-time path dynamic planning of multi-UAV. Full article

The resolution of the unmanned aerial vehicle (UAV) path-planning problem frequently leverages optimization algorithms as a foundational approach. Among these, the recently proposed crayfish optimization algorithm (COA) has garnered significant attention as a promising and noteworthy alternative. Nevertheless, COA’s search efficiency tends to diminish in the later stages of the optimization process, making it prone to premature convergence into local optima. To address this limitation, an improved COA (ICOA) is proposed. To enhance the quality of the initial individuals and ensure greater population diversity, the improved algorithm utilizes chaotic mapping in conjunction with a stochastic inverse learning strategy to generate the initial population. This modification aims to broaden the exploration scope into higher-quality search regions, enhancing the algorithm’s resilience against local optima entrapment and significantly boosting its convergence effectiveness. Additionally, a nonlinear control parameter is incorporated to enhance the algorithm’s adaptivity. Simultaneously, a Cauchy variation strategy is applied to the population’s optimal individuals, strengthening the algorithm’s ability to overcome stagnation. ICOA’s performance is evaluated by employing the IEEE CEC2017 benchmark function for testing purposes. Comparison results reveal that ICOA outperforms other algorithms in terms of optimization efficacy, especially when applied to complex spatial configurations and real-world problem-solving scenarios. The proposed algorithm is ultimately employed in UAV path planning, with its performance tested across a range of terrain obstacle models. The findings confirm that ICOA excels in searching for paths that achieve safe obstacle avoidance and lower trajectory costs. Its search accuracy is notably superior to that of the comparative algorithms, underscoring its robustness and efficiency. ICOA ensures the balanced exploration and exploitation of the search space, which are particularly crucial for optimizing UAV path planning in environments with symmetrical and asymmetrical constraints. Full article

The optimal viewpoint for monitoring robotic production processes is crucial for maintenance, inspection, and error handling, especially in large-scale production facilities, as it maximizes visual information. This paper presents a method for dynamic camera planning using an Unmanned Aerial Vehicle (UAV), enabling collision-free operation and measurable, high perspective coverage for a user-defined Region of Interest (ROI). Therefore, optimal viewpoints are searched with a greedy search algorithm and a decision for the optimal viewpoint is derived. The method is implemented within a simulation framework in Unity and evaluated in a robotic palletizing application. Results show that the use of a UAV as dynamic camera achieves up to twice the perspective coverage during continuous flight compared to the current capabilities of static cameras. Full article

The frequent launches of reusable launch vehicles are currently the primary approach to support large-scale space transportation, necessitating high reliability in recovery flights. This paper proposes a mission re-planning scheme to address throttling faults, which significantly affect the feasibility of powered landing. To quantify the influence of throttling capability, the concept of “controllable set (CS)” is introduced. The CS is defined as the collection of all feasible initial states that can achieve a successful powered landing and is computed using polyhedron approximation and convex optimization. Based on the CS, the physical feasibility of a power landing problem under deviations from the nominal conditions can be evaluated probabilistically. Besides, a deep neural network (DNN) is constructed to enhance the computational efficiency of the CS analysis, thereby meeting the requirements for online applications. Finally, an effective re-planning scheme is proposed to deal with throttling faults in recovery flight. This is achieved by adjusting the designed angle of attack during the endo-atmosphere unpowered descent phase and selecting the associated optimal handover conditions to initiate the powered landing. The optimal re-planning parameters are determined through a comprehensive investigation of the design space, leveraging probability-based CS analysis and computationally efficient DNN predictions. Simulations verify the accuracy of the CS computation algorithm and the effectiveness of the re-planning scheme under different fault conditions. The results indicate high feasibility probabilities of 99.97%, 98.12%, and 78.52% for maximum throttling capabilities at 65%, 75%, and 85% of nominal thrust magnitude, respectively. Full article

Seismic exploration in hard-to-reach hazardous environments like deserts is a very expensive and time-consuming process that involves a lot of human resources and equipment. These difficulties can be overcome with the implementation of robots, providing flexible mission design, safe operation, and high precision data acquisition. This work presents an autonomous robotic system to assist seismic crews in advanced data acquisition for near-surface characterization, shallow cavity detection, and acquisition grid infill. The developed system consists of a swarm control station and a swarm of unmanned aerial vehicles (UAVs) equipped with seismic sensors. The architecture of the swarm control station, its individual blocks, features of UAV exploitation for seismic data acquisition tasks, hardware and software tool limitations are considered. Algorithms for planning UAV swarm flight paths, their comparison and trajectory examples are presented. Experiments utilizing 9 and 16 UAVs to record 171 and 144 target points, respectively, in harsh desert conditions are described. The results demonstrate the feasibility of the proposed system for seismic data acquisition. The developed robotic system offers flexibility in seismic survey design and planning, enabling efficient coverage of vast areas and facilitating comprehensive data acquisition, which enhances the accuracy and resolution of subsurface seismic imaging. Full article

Anthropogenic structures such as overhead powerlines pose potentially high collision risks to flying animals, particularly birds, leading to millions of fatalities each year. Studies of bird collisions with powerlines to date, however, have estimated different numbers of collision per year and per kilometer in highly variable landscapes. This study aimed to clarify the risk of bird collisions with powerlines in an average landscape, to overcome the bias towards studies in collision hotspots. We conducted experiments to determine searcher efficiency, removal, and decomposition rates of collided birds as well as searching for collision victims and recording flight movements and flight reactions towards the powerlines. Annual bird-strike rates and flight phenology were analyzed using generalized additive models (GAMs). We estimated 50.1 collision victims per powerline kilometer per year and demonstrated that pigeons (especially Wood Pigeon, Columba palumbus) accounted for the largest proportion of collision victims (approximately 65%). Our study thus offers the opportunity to estimate the number of bird collisions (and the range of species) that can be expected in areas that are not particularly rich in bird life or sensitive, especially in view of the planned intensive expansion of energy structures in the context of the green energy transition. Full article

Trajectory prediction is critical for ensuring the safety, reliability, and scalability of Unmanned Aerial Vehicle (UAV) in urban environments. Despite advances in deep learning, existing methods often struggle with dynamic UAV conditions, such as rapid directional changes and limited forecasting horizons, while lacking comprehensive real-time validation and generalization capabilities. This study addresses these challenges by proposing a gated recurrent unit (GRU)-based deep learning framework optimized through Look_Back and Forward_Length labeling to capture complex temporal patterns. The model demonstrated state-of-the-art performance, surpassing existing unmanned aerial vehicles (UAV) and aircraft trajectory prediction approaches, including FlightBERT++, in terms of both accuracy and robustness. It achieved reliable long-range predictions up to 4 s, and its real-time feasibility was validated due to its efficient resource utilization. The model’s generalization capability was confirmed through evaluations on two independent UAV datasets, where it consistently predicted unseen trajectories with high accuracy. These findings highlight the model’s ability to handle rapid maneuvers, extend prediction horizons, and generalize across platforms. This work establishes a robust trajectory prediction framework with practical applications in collision avoidance, mission planning, and anti-drone systems, paving the way for safer and more scalable UAV operations. Full article

Before a fixed-wing UAV executes target tracking missions, it is essential to identify targets through reconnaissance mission areas using onboard payloads. This paper presents an autonomous mission planning method designed for such reconnaissance operations, enabling effective target identification prior to tracking. Existing planning methods primarily focus on flight performance, energy consumption, and obstacle avoidance, with less attention to integrating payload. Our proposed method emphasizes the combination of two key functions: flight path planning and payload mission planning. In terms of path planning, we introduce a method based on the Hierarchical Traveling Salesman Problem (HTSP), which utilizes the nearest neighbor algorithm to find the optimal visit sequence and entry points for area targets. When dealing with area targets containing no-fly zones, HTSP quickly calculates a set of waypoints required for coverage path planning (CPP) based on the Generalized Traveling Salesman Problem (GTSP), ensuring thorough and effective reconnaissance coverage. In terms of payload mission planning, our proposed method fully considers payload characteristics such as scan resolution, imaging width, and operating modes to generate predefined mission instruction sets. By meticulously analyzing payload constraints, we further optimized the path planning results, ensuring that each instruction meets the payload performance requirements. Finally, simulations validated the effectiveness and superiority of the proposed autonomous mission planning method in reconnaissance tasks. Full article

Future mobile communication technology will be used to build an integrated global coverage network. Unmanned aerial vehicles (UAVs) are the first choice for low-altitude networks due to their low cost, flexibility, and ease of operation. The characteristics of UAVs also bring new challenges to communication networks, such as short flight time, complex networking, and unstable communication quality. Therefore, it has become an urgent problem to reasonably plan the location of UAV Base Stations (UAV-BSs), reduce communication power consumption, optimize network performance, and build an efficient and stable UAV communication network (UAVCN). The traditional strategy only pays attention to the signal coverage, and ignores the influence of system transmission power on the network, which reduces the performance of the communication system. In this study, a circular coverage power optimization strategy (CCPO) based on system transmit power is proposed. The mathematical model of the circular coverage problem is used to describe the full coverage process of the UAV base station to ground users, and the deployment optimization is carried out with the goal of minimizing system transmit power, so as to obtain an efficient and reliable site planning scheme. In this paper, the binomial power function is used to continuously fit the discrete solution of the circle covering problem, and the circle covering power optimization formula is rearranged. By analyzing the convexity of the objective function under the circular coverage model, the convex optimization theory is used to solve the objective problem, and the optimal deployment number of UAVs and site planning scheme under the circular coverage power optimization strategy are given. Simulation verifies the superiority of the proposed method. Compared with the traditional hexagon strategy and the minimum power loss strategy, the circular coverage power optimization station location planning strategy can save 14.75% and 6.52% of power resources, providing a valuable reference for the design and optimization of UAV communication systems. It provides a promising solution for further improving the performance and efficiency of UAVCNs. Full article

Predicting rice yield accurately is crucial for enhancing farming practices and securing food supplies. This research aims to estimate rice yield in Peru’s Lambayeque region by utilizing spectral and textural indices derived from unmanned aerial vehicle (UAV) imagery, which offers a cost-effective alternative to traditional approaches. UAV data collection in commercial areas involved seven flights in 2022 and ten in 2023, focusing on key growth stages such as flowering, milk, and dough, each showing significant predictive capability. Vegetation indices like NDVI, SP, DVI, NDRE, GNDVI, and EVI2, along with textural features from the gray-level co-occurrence matrix (GLCM) such as ENE, ENT, COR, IDM, CON, SA, and VAR, were combined to form a comprehensive dataset for model training. Among the machine learning models tested, including Multiple Linear Regression (MLR), Support Vector Machines (SVR), and Random Forest (RF), MLR demonstrated high reliability for annual data with an R² of 0.69 during the flowering and milk stages, and an R² of 0.78 for the dough stage in 2022. The RF model excelled in the combined analysis of 2022–2023 data, achieving an R² of 0.58 for the dough stage, all confirmed through cross-validation. Integrating spectral and textural data from UAV imagery enhances early yield prediction, aiding precision agriculture and informed decision-making in rice management. These results emphasize the need to incorporate climate variables to refine predictions under diverse environmental conditions, offering a scalable solution to improve agricultural management and market planning. Full article

Drones are useful tools for performing tasks that are difficult for humans. Thus, they are being increasingly utilized in various fields. In smart construction, a range of methods, including robots and drones, has been proposed to inspect facilities and other similar structures. Global navigation satellite system (GNSS) shadowing can occur when large bridge substructures, which are difficult for humans to access, are inspected using drones because GNSS is a major component in drone operation. This study develops a path planning algorithm to address areas with GNSS shadowing. The operation mode of the drone is classified into waypoint selection based on the photography point algorithm (WPS-PPA) and GNSS non-shadowing area algorithm (WPS-GNSA). Both algorithms are experimentally compared for flight performance in the GNSS shadowing area. A field experiment was conducted by varying the distance between the drone and the bridge substructure and by comparing the success of the flights. In successful flights, the GNSS reception of WPS-GNSA reached 1.4 times that of WPS-PPA. Furthermore, even in failed flights, compared to the WPS-PPA algorithm, the WPS-GNSA algorithm continued flight until the GNSS signal further deteriorated. Accordingly, WPS-GNSA is more favorable than WPS-PPA for inspecting bridge substructures under GNSS signal shadowing. Full article

The problem of allocating multiple UAV tasks is a complex combinatorial optimization challenge, involving various constraints. This paper presents an autonomous multi-UAV cooperative task allocation method based on an improved Great Wall Construction Algorithm. A model integrating battlefield environmental factors, 3D terrain data, and threat assessments is developed for optimized task allocation and trajectory planning. The algorithm is enhanced using a good point set initialization strategy, Gaussian distribution estimation, and a Cauchy reorganization variant. The simulation results show that replacing straight-line distances with actual flight distances leads to more rational mission sequences, improving combat effectiveness under realistic terrain and threat conditions. The enhanced algorithm demonstrates superior accuracy and faster convergence. Full article

The frequent occurrence of forest fires in mountainous regions has posed severe threats to both the ecological environment and human activities. This study proposed a multi-target firefighting task planning method of forest fires by multiple UAVs (Unmanned Aerial Vehicles) integrating task allocation and path planning. The forest fire environment factors such high temperatures, dense smoke, and signal shielding zones were considered as the threats. The multi-UAVs task allocation and path planning model was established with the minimum of flight time, flight angle, altitude variance, and environmental threats. In this process, the study considers only the use of fire-extinguishing balls as the fire suppressant for the UAVs. The improved multi-population grey wolf optimization (MP–GWO) algorithm and non-Dominated sorting genetic algorithm II (NSGA-II) were designed to solve the path planning and task allocation models, respectively. Both algorithms were validated compared with traditional algorithms through simulation experiments, and the sensitivity analysis of different scenarios were conducted. Results from benchmark tests and case studies indicate that the improved MP–GWO algorithm outperforms the grey wolf optimizer (GWO), pelican optimizer (POA), Harris hawks optimizer (HHO), coyote optimizer (CPO), and particle swarm optimizer (PSO) in solving more complex optimization problems, providing better average results, greater stability, and effectively reducing flight time and path cost. At the same scenario and benchmark tests, the improved NSGA-II demonstrates advantages in both solution quality and coverage compared to the original algorithm. Sensitivity analysis revealed that with the increase in UAV speed, the flight time in the completion of firefighting mission decreases, but the average number of remaining fire-extinguishing balls per UAV initially decreases and then rises with a minimum of 1.9 at 35 km/h. The increase in UAV load capacity results in a higher average of remaining fire-extinguishing balls per UAV. For example, a 20% increase in UAV load capacity can reduce the number of UAVs from 11 to 9 to complete firefighting tasks. Additionally, as the number of fire points increases, both the required number of UAVs and the total remaining fire-extinguishing balls increase. Therefore, the results in the current study can offer an effective solution for multiple UAVs firefighting task planning in forest fire scenarios. Full article