Search Results (83)

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

This research focuses on enhancing cooperative localization for heterogeneous mobile robots composed of a quadcopter and an unmanned ground vehicle. The study employs sensor fusion techniques, particularly the Extended Kalman Filter, to fuse data from various sensors, including GPSs, IMUs, and cameras. By integrating these sensors and optimizing fusion strategies, the research aims to improve the precision and reliability of cooperative localization in complex and dynamic environments. The primary objective is to develop a practical framework for cooperative localization that addresses the challenges posed by the differences in mobility and sensing capabilities among heterogeneous robots. Sensor fusion is used to compensate for the limitations of individual sensors, providing more accurate and robust localization results. Moreover, a comparative analysis of different sensor combinations and fusion strategies helps to identify the optimal configuration for each robot. This research focuses on the improvement of cooperative localization, path planning, and collaborative tasks for heterogeneous robots. The findings have broad applications in fields such as autonomous transportation, agricultural operation, and disaster response, where the cooperation of diverse robotic platforms is crucial for mission success. 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

A cooperative game negotiation strategy considering multiple constraints is proposed for distributed impulsive multi-spacecraft approach missions in the presence of defending spacecraft. It is a dual-stage decision-making method that includes offline trajectory planning and online distributed negotiation. In the trajectory planning stage, a relative orbital dynamics model is first established based on the Clohessy–Wiltshire (CW) equations, and the state transition equations for impulsive maneuvers are derived. Subsequently, a multi-objective optimization model is formulated based on the NSGA-II algorithm, utilizing a constraint dominance principle (CDP) to address various constraints and generate Pareto front solutions for each spacecraft. In the distributed negotiation stage, the negotiation strategy among spacecraft is modeled as a cooperative game. A potential function is constructed to further analyze the existence and global convergence of Nash equilibrium. Additionally, a simulated annealing negotiation strategy is developed to iteratively select the optimal comprehensive approach strategy from the Pareto fronts. Simulation results demonstrate that the proposed method effectively optimizes approach trajectories for multi-spacecraft under complex constraints. By leveraging inter-satellite iterative negotiation, the method converges to a Nash equilibrium. Additionally, the simulated annealing negotiation strategy enhances global search performance, avoiding entrapment in local optima. Finally, the effectiveness and robustness of the dual-stage decision-making method were further demonstrated through Monte Carlo simulations. Full article

In recent years, UAV (unmanned aerial vehicle)-UGV (unmanned ground vehicle) collaborative systems have played a crucial role in emergency disaster rescue. To improve rescue efficiency, heterogeneous network and task chain methods are introduced to cooperatively develop rescue sequences within a short time for collaborative systems. However, current methods also overlook resource overload for heterogeneous units and limit planning to a single task chain in cross-platform rescue scenarios, resulting in low robustness and limited flexibility. To this end, this paper proposes Gemini, a cascaded dual-agent deep reinforcement learning (DRL) framework based on the Heterogeneous Service Network (HSN) for multiple task chains planning in UAV-UGV collaboration. Specifically, this framework comprises a chain selection agent and a resource allocation agent: The chain selection agent plans paths for task chains, and the resource allocation agent distributes platform loads along generated paths. For each mission, a well-trained Gemini can not only allocate resources in load balancing but also plan multiple task chains simultaneously, which enhances the robustness in cross-platform rescue. Simulation results show that Gemini can increase rescue effectiveness by approximately 60% and improve load balancing by approximately 80%, compared to the baseline algorithm. Additionally, Gemini’s performance is stable and better than the baseline in various disaster scenarios, which verifies its generalization. Full article

Space object detection, as the foundation for ensuring the long-term safe and stable operation of spacecraft, is widely applied in a variety of close-proximity tasks such as non-cooperative target monitoring, space debris avoidance, and spacecraft mission planning. To strengthen the detection capabilities for non-cooperative spacecraft and space debris, a method based on You Only Look Once Version 11 (YOLO11) is proposed in this paper. On the one hand, to tackle the issues of noise and low contrast in images captured by spacecraft, bilateral filtering is applied to remove noise while preserving edge and texture details effectively, and image contrast is enhanced using the contrast-limited adaptive histogram equalization (CLAHE) technique. On the other hand, to address the challenge of small object detection in spacecraft, loss-guided online data augmentation is proposed, along with improvements to the YOLO11 network architecture, to boost detection capabilities for small objects. The experimental results show that the proposed method achieved $99.0\%$ mAP50 (mean Average Precision with an Intersection over Union threshold of 0.50) and $92.6\%$ mAP50-95 on the SPARK-2022 dataset, significantly outperforming the YOLO11 baseline, thereby validating the effectiveness of the proposed method. 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

Cooperative operations of Unmanned Underwater Vehicles (UUVs) have extensive applications in fields such as marine exploration, ecological observation, and subsea security. Path planning, as a key technology for UUV autonomous navigation, is crucial for enhancing the adaptability and mission execution efficiency of UUVs in complicated marine environments. However, existing methods still have significant room for improvement in handling obstacles, multi-task coordination, and other complex problems. In order to overcome these issues, we put forward a task allocation and path planning method for UUVs. First, we introduce a task allocation mechanism based on an Improved Grey Wolf Algorithm (IGWA). This mechanism comprehensively considers factors such as target value, distance, and UUV capability constraints to achieve efficient and reasonable task allocation among UUVs. To enhance the search efficiency and accuracy of task allocation, a Circle chaotic mapping strategy is incorporated into the traditional GWA to improve population diversity. Additionally, a differential evolution mechanism is integrated to enhance local search capabilities, effectively mitigating premature convergence issues. Second, an improved RRT* algorithm termed GR-RRT* is employed for UUV path planning. By designing a guidance strategy, the sampling probability near target points follows a two-dimensional Gaussian distribution, ensuring obstacle avoidance safety while reducing redundant sampling and improving planning efficiency. Experimental results demonstrate that the proposed task allocation mechanism and improved path planning algorithm exhibit significant advantages in task completion rate and path optimization efficiency. Full article

This paper proposes a task planning method that integrates deep Q-learning network (DQN) with matrix sorting for Earth-oriented static multi-target cooperative observation tasks. The approach addresses emergent satellite failures in imaging constellations by eliminating the need for network model retraining during satellite malfunctions. It enables real-time generation of optimal task allocation schemes in contingency scenarios, ensuring efficient and adaptive task planning. Firstly, a mission scenario model is established by formulating task constraints and defining optimization objectives; secondly, a deep reinforcement learning framework is constructed to output the observation target sequence; then, the observation target sequence is transformed into a target sequence matrix, and a matrix-sorting planning method is proposed to carry out the optimal assignment of the task; lastly, a replanning method is designed for sudden satellite failure and insertion of urgent tasks. The experimental results show that the method has fast task planning speed, high task completion, and immediate task replanning capability. 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

This paper focuses on the coupled optimization problem of path optimization and task assignment for UAVs mounted on mobile platforms. Combining the UAV turning angle, minimum direct flight trajectory and other flight characteristics, the path optimization model on the 3D raster map is established with the objectives of shortest flight time and minimum UAV destruction, and the optimal path between the vertices of each mission is derived by using an improved Gray Wolf Optimization algorithm. Combining the takeoff and landing time-window constraints with the range and mission resource constraints, this mission planning model is established with the objective of maximizing the efficiency ratio of mission revenue–UAV damage consumption. Combining the optimal paths between vertices, a complete UAV flight path is formed, which provides a path optimization and goal assignment method for UAV clusters mounted on mobile platforms to perform multiple tasks cooperatively, and its feasibility and effectiveness are verified through simulation experiments. Full article

Unmanned aerial vehicle (UAV) swarms have shown substantial potential to enhance operational efficiency and reduce strike costs, presenting extensive applications in modern urban warfare. However, achieving effective strike performance in complex urban environments remains challenging, particularly when considering three-dimensional obstacles and threat zones simultaneously, which can significantly degrade strike effectiveness. To address this challenge, this paper proposes a target strike strategy using the Electric Eel Foraging Optimization (EEFO) algorithm, a heuristic optimization method designed to ensure precise strikes in complex environments. The problem is formulated with specific constraints, modeling each UAV as an electric eel with random initial positions and velocities. This algorithm simulates the interaction, resting, hunting, and migrating behaviors of electric eels during their foraging process. During the interaction phase, UAVs engage in global exploration through communication and environmental sensing. The resting phase allows UAVs to temporarily hold their positions, preventing premature convergence to local optima. In the hunting phase, the swarm identifies and pursues optimal paths, while in the migration phase the UAVs transition to target areas, avoiding threats and obstacles while seeking safer routes. The algorithm enhances overall optimization capabilities by sharing information among surrounding individuals and promoting group cooperation, effectively planning flight paths and avoiding obstacles for precise strikes. The MATLAB(R2024b) simulation platform is used to compare the performance of five optimization algorithms—SO, SCA, WOA, MFO, and HHO—against the proposed Electric Eel Foraging Optimization (EEFO) algorithm for UAV swarm target strike missions. The experimental results demonstrate that in a sparse undefended environment, EEFO outperforms the other algorithms in terms of trajectory planning efficiency, stability, and minimal trajectory costs while also exhibiting faster convergence rates. In densely defended environments, EEFO not only achieves the optimal target strike trajectory but also shows superior performance in terms of convergence trends and trajectory cost reduction, along with the highest mission completion rate. These results highlight the effectiveness of EEFO in both sparse and complex defended scenarios, making it a promising approach for UAV swarm operations in dynamic urban environments. Full article

The challenge of search inefficiency arises when multiple UAV swarms conduct dynamic target area searches in unknown environments. The primary sources of this inefficiency are repeated searches in the target region and the dynamic motion of targets. To address this issue, we present the distributed adaptive real-time planning search (DAPSO) technique, which enhances the search efficiency for dynamic targets in uncertain mission situations. To minimize repeated searches, UAVs utilize localized communication for information exchange and dynamically update their situational awareness regarding the mission environment, facilitating collaborative exploration. To mitigate the effects of target mobility, we develop a dynamic mission planning method based on local particle swarm optimization, enabling UAVs to adjust their search trajectories in response to real-time environmental inputs. Finally, we propose a distance-based inter-vehicle collision avoidance strategy to ensure safety during multi-UAV cooperative searches. The experimental findings demonstrate that the proposed DAPSO method significantly outperforms other search strategies regarding the coverage and target detection rates. Full article

Unmanned aerial vehicles (UAVs) can collaborate as teams to accomplish diverse mission objectives, such as target search and tracking. This paper introduces a method that leverages accumulated target-density information over the course of a UAV mission to adapt path-planning rewards, guiding UAVs toward areas with a higher likelihood of target presence. The target density is modeled using a Gaussian process, which is iteratively updated as the UAVs search the environment. Unlike conventional search algorithms that prioritize unexplored regions, this approach incentivizes revisiting target-rich areas. The target-density information is shared across UAVs using decentralized consensus filters, enabling cooperative path selection that balances the exploration of uncertain regions with the exploitation of known high-density areas. The framework presented in this paper provides an adaptive cooperative search method that can quickly develop an understanding of the region’s target-dense areas, helping UAVs refine their search. Through Monte Carlo simulations, we demonstrate this method in both a 2D grid region and road networks, showing up to a 26% improvement in target density estimates. Full article

In this paper, we introduce a fault-tolerant multi-agent reinforcement learning framework called SERT-DQN to optimize the operations of UAVs with UGV central control in coverage path planning missions. Our approach leverages dual learning systems that combine individual agent autonomy with centralized strategic planning, thus enhancing the efficiency of cooperative path planning missions. This framework is designed for high performance in environments with fault uncertainty detected and operational challenges such as interruptions in connectivity and compromised sensor reliability. With the integration of an innovative communication system between agents, our system appropriately handles both static and dynamic environments. Also, we introduce similarity-based shared experience replay to attain faster convergence and sample efficiency in the multi-agent system. The architecture is specially designed to respond adaptively to such irregularities by effectively showing enhanced resilience in scenarios where data integrity is impaired due to faults or the UAV faces disruptions. Simulation results indicate that our fault tolerance algorithms are very resilient and do indeed improve mission outcomes, especially under dynamic and highly uncertain operating conditions. This approach becomes critical for the most recent sensor-based research in autonomous systems. Full article