Search Results (75)

Due to the limited endurance of UAVs, especially in scenarios involving large areas and dense target nodes, it is challenging for multiple UAVs to complete diverse tasks while ensuring timely execution. Toward this, we propose a cross-platform system consisting of an aircraft carrier (AC) and multiple UAVs, which makes unified task planning for included heterogeneous platforms to maximize the efficiency of the entire combat system. The carrier-based UAV swarm mission planning problem is formulated to minimize completion time and resource utilization, taking into account large-scale targets, multi-type tasks, and multi-obstacle environments. Since the problem is complex, we design a decoupled framework to simplify the solution by decomposing it into two levels: upper-level AC path planning and bottom-level multi-UAV cooperative mission planning. At the upper level, a drop point determination method and a discrete genetic algorithm incorporating improved A* (DGAIIA) are proposed to plan the AC’s path in the presence of no-fly zones and radar threats. At the bottom level, an improved differential evolution algorithm with a market mechanism (IDEMM) is proposed to minimize task completion time and maximize UAV utilization. Specifically, a dual-switching search strategy and a neighborhood-first buying-and-selling mechanism are developed to improve the search efficiency of the IDEMM. Simulation results validate the effectiveness of both the DGAIIA and IDEMM. An animation of the simulation results is available at simulation section. Full article

Advancements in Unmanned Aerial Vehicle (UAV) technologies have increased exponentially in recent years, with UAV swarm being a key area of interest. UAV swarm overcomes the energy reserve, payload, and single-objective limitations of single UAVs, enabling broader mission scopes. Despite these advantages, UAV swarm has yet to see widespread application within global industry. A leading factor hindering swarm application within industry is the divide that currently exists between the functional capacity of modern UAV swarm systems and the functionality required by legislation. This paper investigates this divide through an overview of global legislative practice, contextualized via a case study of Australia’s UAV regulatory environment. The overview highlighted legislative objectives that coincided with open challenges in the UAV swarm literature. These objectives were then formulated into analysis criteria that assessed whether systems presented sufficient functionality to address legislative concern. A systematic review methodology was used to apply analysis criteria to multi-objective UAV swarm mission planning systems. Analysis focused on multi-objective mission planning systems due to their role in defining the functional capacity of UAV swarms within complex real-world operational environments. This, alongside the popularity of these systems within the modern literature, makes them ideal candidates for defining new enabling technologies that could address the identified areas of weakness. The results of this review highlighted several legislative considerations that remain under-addressed by existing technologies. These findings guided the proposal of enabling technologies to bridge the divide between functional capacity and legislative concern. Full article

With the global increase in maritime activities, the frequency of maritime accidents has risen, underscoring the urgent need for faster and more efficient search and rescue (SAR) solutions. This study presents an intelligent unmanned aerial vehicle (UAV)-based maritime rescue system that combines GPS-driven dynamic path planning with vision-based dual-target detection and tracking. Developed within the Gazebo simulation environment and based on modular ROS architecture, the system supports stable takeoff and smooth transitions between multi-rotor and fixed-wing flight modes. An external command module enables real-time waypoint updates. This study proposes three path-planning schemes based on the characteristics of drones. Comparative experiments have demonstrated that the triangular path is the optimal route. Compared with the other schemes, this path reduces the flight distance by 30–40%. Robust target recognition is achieved using a darknet-ROS implementation of the YOLOv4 model, enhanced with data augmentation to improve performance in complex maritime conditions. A monocular vision-based ranging algorithm ensures accurate distance estimation and continuous tracking of rescue vessels. Furthermore, a dual-target-tracking algorithm—integrating motion prediction with color-based landing zone recognition—achieves a 96% success rate in precision landings under dynamic conditions. Experimental results show a 4% increase in the overall mission success rate compared to traditional SAR methods, along with significant gains in responsiveness and reliability. This research delivers a technically innovative and cost-effective UAV solution, offering strong potential for real-world maritime emergency response applications. Full article

Unmanned Aerial Vehicle (UAV) path planning is critical for ensuring flight safety and enhancing mission execution efficiency. This problem is typically formulated as a complex, multi-constrained, and nonlinear optimization task, often addressed using meta-heuristic algorithms. The Crested Porcupine Optimizer (CPO) has become an excellent method to solve this problem; however, the standard CPO has limitations, such as the lack of adaptive parameter tuning to adapt to complex environments, slow convergence, and the tendency to fall into local optimal solutions. To address these issues, this paper proposes an algorithm named QCPO, which integrates CPO with Q-learning to improve UAV path optimization performance. Q-learning is employed to adaptively adjust the key parameters of the CPO, thereby overcoming the limitations of traditional fixed-parameter settings. Inspired by the porcupine’s defense mechanisms, a novel audiovisual coordination strategy is introduced to balance visual and auditory responses, accelerating convergence in the early optimization stages. A refined position update mechanism is designed to prevent excessive step sizes and boundary violations, enhancing the algorithm’s global search capability. A B-spline-based trajectory smoothing method is also incorporated to improve the feasibility and smoothness of the planned paths. In this paper, we compare QCPO with four outstanding heuristics, and QCPO achieves the lowest path cost in all three test scenarios, with path cost reductions of 30.23%, 26.41%, and 33.47%, respectively, compared to standard CPO. The experimental results confirm that QCPO offers an efficient and safe solution for UAV path planning. Full article

With the increasing adoption of cooperative multi-UAV systems in applications such as cargo delivery and ground reconnaissance, the demand for scalable and efficient path planning methods has grown substantially. However, traditional heuristic algorithms are frequently trapped in local optima, require task-specific manual tuning, and exhibit limited generalization capabilities. Furthermore, their dependence on iterative optimization renders them unsuitable for large-scale real-time applications. To address these challenges, this paper introduces an end-to-end deep reinforcement learning framework that bypasses the reliance on handcrafted heuristic rules. The proposed method leverages an encoder–decoder architecture with multi-head attention (MHA), where the encoder generates embeddings for UAVs and task parameters, while the decoder dynamically selects actions based on contextual embeddings and enforces feasibility through a masking mechanism. The MHA module effectively models global spatial-task dependencies among nodes, enhancing solution quality. Additionally, we integrate a Multi-Start Greedy Rollout Baseline to evaluate diverse trajectories via parallelized greedy searches, thereby reducing policy gradient variance and improving training stability. Experiments demonstrated significant improvements in scalability, particularly in 100-node scenarios, where our method drastically reduced inference time compared to conventional methods, while maintaining a competitive path cost efficiency. A further validation on simulated mission environments and real-world geospatial data (sourced from Google Earth) underscored the robust generalization of the framework. This work advances large-scale UAV mission planning by offering a scalable, adaptive, and computationally efficient solution. Full article

Maritime unmanned aerial vehicles (UAVs) equipped with inertial navigation systems (INS) are prone to error accumulation, which can lead to excessive positioning errors and hinder their ability to perform long distance missions. To address this issue, this study first constructs a directed graph network for a flight area based on start and end points as well as error correction points. A multi-objective route planning model is then developed for a UAV, aiming to minimize both the flight distance and the number of positioning corrections. Considering the UAV’s turning radius, a trajectory length calculation model based on 3D Dubins curves is designed. Subsequently, a forward labeling-based multi-objective path planning algorithm is proposed to develop an optimization model. Experimental results demonstrate that the proposed method can effectively constrain the UAV’s horizontal and vertical positioning errors within 2.5 m, while optimally balancing flight distance and positioning accuracy to ensure the successful execution of long-range maritime UAV missions. The comparative results demonstrate that, while satisfying the positioning error requirements, our proposed method achieves a reduction of over 1.5% in total flight distance for maritime UAVs compared to the NSGA-II algorithm. Full article

Multi-UAV path planning presents a critical challenge in Unmanned Aerial Vehicle (UAV) applications, particularly in environments with various obstacles and restrictions. These conditions transform multi-UAV path planning into a complex optimization problem with multiple constraints, significantly reducing the number of feasible solutions and complicating the generation of optimal flight trajectories. Although the slime mold algorithm (SMA) has proven effective in optimization missions, it still suffers from limitations such as inadequate exploration capacity, premature convergence, and a propensity to become stuck in local optima. These drawbacks degrade its performance in intricate multi-UAV scenarios. This study proposes a self-adaptive improved slime mold algorithm called AI-SMA to address these issues. Firstly, AI-SMA incorporates a novel search mechanism to balance exploration and exploitation by integrating ranking-based differential evolution (rank-DE). Then, a self-adaptive switch operator is introduced to increase population diversity in later iterations and avoid premature convergence. Finally, a self-adaptive perturbation strategy is implemented to provide an effective escape mechanism, facilitating faster convergence. Extensive experiments were conducted on the CEC 2017 benchmark test suite and multi-UAV path-planning scenarios. The results show that AI-SMA improves the quality of optimal fitness by approximately $7.83\%$ over the original SMA while demonstrating superior robustness and effectiveness in generating collision-free trajectories. 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

Autonomous unmanned aerial vehicle (UAV) swarm networks (UAVSNs) can efficiently perform surveillance, connectivity, computing, and energy transfer services for ground users (GUs). These missions require trajectory planning, UAV-GUs association, task offloading, next-hop selection, and resource allocation, including transmit power, bandwidth, timeslots, caching, and computing resources, to enhance network performance. Owing to the highly dynamic topology, limited resources, stringent quality of service requirements, and lack of global knowledge, optimizing network performance in UAVSNs is very intricate. To address this, an adaptive joint optimization framework is required to handle both discrete and continuous decision variables, ensuring optimal performance under various dynamic constraints. A multi-agent deep reinforcement learning-based adaptive actor–critic framework offers an effective solution by leveraging its ability to extract hidden features through agent interactions, generate hybrid actions under uncertainty, and adaptively learn with scalable generalization in dynamic conditions. This paper explores the recent evolutions of actor–critic frameworks to deal with joint optimization problems in UAVSNs by proposing a novel taxonomy based on the modifications in the internal actor–critic neural network structure. Additionally, key open research challenges are identified, and potential solutions are suggested as directions for future research in UAVSNs. 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

Due to their high flexibility, low cost, and energy-saving advantages, applying Unmanned Aerial Vehicles (UAVs) in logistics is a promising field to achieve better social and economic benefits. Since UAVs’ energy storage capacity is generally low, it is essential to reduce energy costs to improve their system’s energy efficiency. In this paper, we proposed a novel trajectory planning framework to achieve the optimal trajectory with the minimum amount of energy consumption under the constraints of obstacles in a static environment. Based on UAV dynamics, we first derived the required power functions of multi-rotor UAVs in vertical and horizontal flight. To generate a feasible trajectory, we first adopted the A* algorithm to find a path and developed a safe flight corridor for the UAV to fly across by expanding the waypoints against the environment, and then proposed a time-discretization method to formulate the trajectory generation problem and solve it by the convex optimization algorithm. The optimization results in a static environment with obstacles demonstrated that the proposed method could efficiently and effectively obtain the optimal trajectory with the minimum amount of energy consumption under different allowed mission times and payloads. The framework would promote a variety of logistics UAV applications relevant to trajectory planning. Full article

Efficient path planning is vital for multi-UAV inspection missions, yet the comparative effectiveness of different optimization strategies has not received much attention. This paper introduces the first application of the Genetic Algorithm (GA) and Hill Climbing (HC) to multi-UAV inspection of indoor pipelines, providing a unique comparative analysis. GA exemplifies the global search strategy, while HC illustrates an enhanced stochastic local search. This comparison is impactful as it highlights the trade-offs between exploration and exploitation—two key challenges in multi-UAV path optimization. It also addresses practical concerns such as workload balancing and energy efficiency, which are crucial for the successful implementation of UAV missions. To tackle common challenges in multi-UAV operations, we have developed a novel repair mechanism. This mechanism utilizes problem-specific repair heuristics to ensure feasible and valid solutions by resolving redundant or missed inspection points. Additionally, we have introduced a penalty-based approach in HC to balance UAV workloads. Using the Crazyswarm simulation platform, we evaluated GA and HC across key performance metrics: energy consumption, travel distance, running time, and maximum tour length. The results demonstrate that GA achieves a 22% reduction in travel distance and a 23% reduction in energy consumption compared to HC, which often converges to suboptimal solutions. Additionally, GA outperforms HC, Greedy, and Random strategies, delivering at least a 13% improvement in workload balancing and other metrics. These findings establish a novel and impactful benchmark for comparing global and local optimization strategies in multi-UAV tasks, offering researchers and practitioners critical insights for selecting efficient and sustainable approaches to UAV operations in complex inspection environments. Full article

As Unmanned Aerial Vehicles (UAVs) are becoming crucial in modern warfare, research on autonomous path planning is becoming increasingly important. The conflicting nature of the optimization objectives characterizes path planning as a multi-objective optimization problem. Current research has predominantly focused on developing new optimization algorithms. Although being able to find the mathematical optimum is important, one also needs to ensure this optimum aligns with the decision-maker’s (DM’s) most preferred solution (MPS). In particular, to align these, one needs to handle the DM’s preferences on the relative importance of each optimization objective. This paper provides a comprehensive overview of all preference handling techniques employed in the military UAV path planning literature over the last two decades. It shows that most of the literature handles preferences by the overly simplistic method of scalarization via weighted sum. Additionally, the current literature neglects to evaluate the performance (e.g., cognitive validity and modeling accuracy) of the chosen preference handling technique. To aid future researchers handle preferences, we discuss each employed preference handling technique, their implications, advantages, and disadvantages in detail. Finally, we identify several directions for future research, mainly related to aligning the mathematical optimum to the MPS. Full article

The auction mechanism stands as a pivotal distributed solution approach for addressing the task allocation problem in unmanned aerial vehicle (UAV) swarms, with its rapid solution capability well-suited to meet the real-time requirements of aerial mission planning for UAV swarms. Building upon the auction mechanism, this paper proposes a distributed task allocation method for multi-UAV grounded in swarm benefits optimization. The method introduces individual benefit variation to quantify the effect of a task on the benefit of a single UAV, thereby enabling direct optimization of swarm benefit through these individual benefit variations. Within the formulated individual benefit calculation, both the spatial distance between tasks and UAVs and the initial task value along with its temporal decay are taken into account, ensuring a thorough and accurate assessment. Additionally, the method incorporates real-time updates of individual benefits for each UAV, reflecting the dynamic state of task benefit fluctuations within the swarm. Monte Carlo simulation experiments demonstrate that, for a swarm size of 16 UAVs and 80 tasks, the proposed method achieves an average swarm benefit improvement of approximately 2% and 4% over the Consensus-Based Bundle Algorithm (CBBA) and Performance Impact (PI) methods, respectively, thus validating its effectiveness. Full article