Search Results (287)

To address the challenges of dynamic obstacles, limited perception, and multi-UAV coordination constraints in complex urban environments, a hierarchical control framework based on a virtual leader-follower architecture is proposed, covering global planning, local obstacle avoidance, and formation coordination. In the global planning layer, a dynamic adaptive strategy rapidly exploring random tree star (DASRRT*) algorithm is proposed. To address the low sampling efficiency and limited path extension in dense environments that affect traditional RRT*, a hybrid guided sampling strategy, inefficient node optimization strategy, and perception-based adaptive step size strategy are designed. Additionally, a multi-objective cost function is introduced to provide smoother trajectories that better comply with dynamic constraints for trajectory tracking. In the local obstacle-avoidance layer, a distributed controller is constructed based on an improved artificial potential field method, integrating collision avoidance control laws derived from a spring-damper model, dynamic obstacle-avoidance laws that account for obstacle velocities, and formation coordination control laws grounded in consensus theory. In the coordination control layer, a real-time local target selection strategy is established to guide the virtual leader to precisely track the global path, and a dual-mode switching mechanism based on environmental complexity is constructed to dynamically adjust the priority between formation maintenance and autonomous obstacle-avoidance tasks. Comparative experimental results show that the proposed DASRRT* algorithm reduces path planning time by an average of 34.78% and shortens path length by 1.15%. Simulation results for formation flight demonstrate that the proposed hierarchical control framework can adaptively adjust control modes in response to changes in environmental complexity, exhibiting strong adaptability to complex environments and a good ability to generalize to various scenes. Full article

UAV path planning in complex three-dimensional obstacle environments requires a balance between search efficiency and flight feasibility. However, existing RRT*-based methods often fail to satisfy this requirement, as their random sampling lacks directional guidance and makes limited use of environmental information. To this end, this paper proposes an environment-aware cooperative bidirectional RRT* algorithm (EAC-Bi-RRT*). In the sampling stage, the sampling probability of each direction is adaptively adjusted according to the obstacle distribution across 26 directional sectors and the relative goal orientation, so that the search receives stronger directional guidance. During bidirectional expansion, the two trees are assigned leader and follower roles according to the local expandability on the start and goal sides, and their cooperative search is combined with an environment-adaptive step size and a climbing-angle constraint to balance search efficiency and flight reachability. When an expanding node approaches an obstacle, a repulsive-only local directional correction suppresses oscillation, and the initial path is then smoothed by a curvature-constrained B-spline to form a continuous flight trajectory. Across all test scenarios, EAC-Bi-RRT* achieves a 100% planning success rate. Compared with the baseline algorithms, it reduces planning time by approximately 54–90% and path length by approximately 5–18% while maintaining low average turning angles, which demonstrates competitive overall performance. Full article

The safe deployment of multiple Unmanned Aerial Vehicles (UAVs) in complex urban environments relies heavily on accurate environmental perception and efficient cooperative path planning. However, executing multi-UAV operations in low-altitude airspaces faces severe challenges due to the dual constraints of complex building clusters and steady-state wind field disturbances. These dynamic environmental factors frequently distort sensory expectations, inducing trajectory drift and degrading policy robustness. To address these limitations, this paper proposes an enhanced Dueling Double Deep Q-Network (D3QN) algorithm, termed NPD3QN, tailored for perception-aware multi-UAV cooperative path planning. By formulating the perceived environmental data (e.g., wind speed, obstacle distances, and inter-UAV states) into a Markov Decision Process, an N-step update strategy is integrated to enhance the characterization of long-term returns. Simultaneously, an improved Prioritized Experience Replay (PER) mechanism is developed to actively filter negative experiences and assign dynamic weights to critical state-action samples, thereby significantly elevating training stability. A 3D urban kinematic environment incorporating a steady-state simulated wind field is constructed. Extensive ablation and comparative results demonstrate that NPD3QN effectively maps high-dimensional state perceptions to robust control commands. In wind-disturbed scenarios, it generates highly streamlined cooperative trajectories, reducing the total path length by approximately 11.7% compared to the standard D3QN baseline. While currently evaluated within steady-state simulated constraints, this study establishes a robust, sensor-driven methodological foundation for autonomous multi-UAV cooperative path planning in wind-disturbed airspaces. Full article

The dynamic and complex environment, together with challenging assignments, requires that unmanned aerial vehicle (UAV) systems evolve toward cooperation, autonomy, and cognition. UAV swarms illustrate a revolutionary development in aerial robotics, which utilizes coordinated autonomy to improve operational efficiency. This study offers a detailed examination of UAV swarm systems, the latest developments, and their different applications. The main domains, such as intelligent path planning, work allocation, coordinated control, and safety issues, are analyzed, focusing on the integration of Artificial Intelligence (AI) and Deep Learning (DL) to enhance decision-making and agility. We address the constraints and potential advances in the field of swarm intelligence to facilitate additional research endeavors. The ongoing advancement of drone swarm technologies and its exploration of military uses highlight the increasing importance of anti-drone swarm strategies. Therefore, studying these strategies will have substantial practical importance in preventing and countering drone swarm combat. Thus, this article provides detailed drone swarm applications and the importance of anti-drone swarm techniques in strategic operations. Furthermore, this comprehensive study of the literature aims to offer innovative perspectives on the latest advances in UAV swarm intelligence technology. Future research trends and challenges are discussed to find the research gap. Full article

To address the challenges of strong coupling between resource allocation and collaborative scheduling in vehicle–UAV cooperative inspections of power distribution lines, as well as the difficulty in balancing efficiency and stability, this paper proposes a path-scheduling feedback-based evolutionary cooperative optimization method. First, an integrated modeling framework for resource allocation and execution scheduling is constructed, incorporating vehicle path decisions and drone task scheduling into a unified optimization space. Next, a feedback-driven two-layer multi-objective evolutionary collaborative optimization algorithm (FB-MOC²) is introduced. The outer layer performs evolutionary search for adaptive resource allocation, while the inner layer solves path planning and collaborative scheduling, with dynamic resource adjustments achieved through execution-layer feedback, forming a data-driven adaptive optimization process. Subsequently, sensitivity analysis is conducted on resource deployment mechanisms, revealing phased evolutionary patterns between resource scale and system performance, and identifying the effective operational range for resource allocation. Finally, the algorithm’s robustness is validated under multiple failure scenarios. Simulation results demonstrate that the proposed method reduces total operation time from 412 min to 315 min, improves battery utilization to 78.5%, and maintains recovery costs within 1.65 times the baseline even under high drone failure rates, while ensuring full inspection coverage. This approach provides an effective bio-inspired and data-driven solution for adaptive resource allocation and robust scheduling in intelligent power distribution line inspections. Full article

This study proposes an integrated task–path cooperative optimization method to address the suboptimal solutions caused by decoupled task allocation and path planning for grouped multi-USV formations. First, an integrated optimization model is established within a hierarchical dynamic closed-loop framework, incorporating a persistent ocean current disturbance of 0.12 m/s to ensure practical environmental realism. Furthermore, efficient solution algorithms are developed: an enhanced Hungarian algorithm for task allocation and a Sine Cosine Algorithm-optimized Artificial Potential Field (SCA-APF) method to resolve local minima. The simulation results demonstrate that the proposed method reduces the weighted total cost by 11.1% and improves task allocation efficiency by over 80.5% compared to improved genetic algorithms. In dynamic environments, the framework achieves an over 99% task completion rate. Crucially, the system maintains real-time responsiveness with per-step computation times below 0.1 s even for a swarm size of N = 32, proving its scalability and suitability for large-scale maritime coordination. Full article

The rapid advancement of unmanned aerial vehicle (UAV) technology has led to its widespread adoption in military reconnaissance, disaster monitoring, environmental inspection, and related fields. However, a single UAV often faces limitations when executing large-scale and complex missions. UAV swarm technology, which employs multi-agent collaboration, can significantly improve task execution efficiency and overall system performance, representing an area of considerable research importance. Current studies on task allocation and path planning for UAV swarms exhibit certain shortcomings, particularly the high computational complexity and insufficient real-time performance of existing path planning methods when applied to highly dynamic, multi-objective, and large-scale complex scenarios. To address the above challenge, this paper proposes a Gale-Shapley-based Genetic Algorithm (GSGA) for UAV swarm task allocation and path planning. First, a multi-UAV data inspection system model is formulated based on an energy consumption model, analyzing the influence of factors including geographical fairness, data utility, and energy consumption. The proposed GSGA integrates the Gale-Shapley stable matching algorithm for one-to-one task assignment between UAVs and sub-regions with a genetic algorithm optimized for intra-region path planning. Dynamic programming is further employed to refine the flight paths. The results show that the GSGA strategy can effectively improve the balance of task allocation, optimize path length and inspection quality. The proposed method demonstrated robust performance in complex scenarios characterized by numerous task targets and intricate regional partitions, consistently enabling UAVs to complete inspection tasks with high collaborative efficiency. Full article

UAV path planning in complex 3D terrain faces the dual challenges of computational efficiency and reliable obstacle avoidance. To address these issues, this paper proposes a Keypoint–Sparse Cache (KSC) strategy and a hierarchical KSC-PPO (Proximal Policy Optimization) framework for mountainous environments with both static terrain and dynamic obstacles. The KSC strategy reduces search complexity through orthogonal slice-based sparse keypoint extraction and path caching reuse, thereby improving the efficiency of global path planning. On this basis, PPO-based local obstacle avoidance is activated only when safety thresholds are exceeded, while the remaining path is replanned globally after threat clearance, which confines avoidance computation to a local scope while preserving global path quality. Experiments in static mountainous environments show that KSC requires substantially less computation time than RRT* and Informed RRT* while maintaining competitive path efficiency, and it also outperforms four bio-inspired optimization algorithms across terrains of increasing complexity. Hybrid navigation validation experiments further show that KSC-PPO achieves high mission success, low collision rates, and low avoidance overhead in dynamic mountainous environments. Experiments demonstrate that KSC-PPO decomposes exponential global search space into controllable linear subproblems, significantly enhancing efficiency while ensuring path quality, providing an effective solution for UAV navigation in complex terrain. Full article

Mobile robot path planning in complex environments remains challenging due to obstacle constraints, high-dimensional search space, and the need to balance path optimality and safety. To address these challenges, this paper proposes an improved Red-Billed Blue Magpie Optimizer (IRBMO) with multi-strategy cooperation. Specifically, a territorial awareness mechanism enhances global exploration to avoid premature path convergence, a representative individual learning strategy improves exploitation to refine path quality, and a random subpopulation diffusion strategy helps escape local optima in complex obstacle environments. The proposed method is applied to grid-based path planning problems with different map sizes and obstacle densities. Experimental results show that IRBMO significantly reduces path length compared with other algorithms, while achieving faster convergence and better stability. Parameter sensitivity analysis, ablation study, and convergence analysis further verify the effectiveness of the proposed strategies. In addition, benchmark tests on CEC2017 and CEC2022 functions against 19 competitors further confirm its optimization capability. Overall, IRBMO provides an effective and robust solution for robot path planning problems. Full article

The cooperative airdrop of UAV swarms by multiple transport aircraft creates a large-scale multi-agent planning problem. The mission involves heterogeneous aircraft, multi-visit airdrop areas, strict time windows, and threat-aware flight paths. To address these challenges, this work develops an integrated framework for both global task allocation and real-time replanning in complex three-dimensional operational environments. First, for the combinatorial optimization of task execution sequences across multiple aircraft, a static task assignment method is proposed. This method employs a Hybrid-encoding Constrained Black-winged Kite Algorithm (HCBKA), which incorporates optimization metrics such as mission execution time, completion rate, and load-balancing symmetry among aircraft. The HCBKA aims to find a task assignment scheme that achieves a comprehensive optimum across multiple objectives through efficient model solving. Second, to handle potential real-time dynamic changes during mission execution, a rapid-response and generalizable replanning mechanism is developed. This mechanism utilizes an event-triggered strategy based on a Time-window aware Dynamic Auction Algorithm (TDAA). It ensures that the system can promptly initiate and execute online task reallocation in response to contingencies such as changing mission requirements or losses within its own drone swarm, thus maintaining the adaptability and robustness of the overall plan. Simulation results show that the proposed framework produces high-quality global solutions and maintains strong robustness under dynamic changes. The approach provides an effective and scalable solution for coordinated multi-aircraft swarm airdrop missions. Full article

Unmanned Aerial Vehicle (UAV) tracking of ground moving targets holds significant applications in domains such as intelligent transportation, logistics distribution, and environmental monitoring, placing greater demands on efficient and stable path-planning methods for vehicular tracking. This study investigates a UAV path tracking approach based on a deep reinforcement learning algorithm, Proximal Policy Optimization (PPO). Starting from the kinematic characteristics of UAVs and ground vehicles, a 3D path planning model was constructed that considers spatial coordinates, velocity, and attitude constraints. A well-designed objective function—including tracking error minimization, energy optimization, and safety distance constraints—was incorporated. By designing the state space, action space, and reward function, the PPO algorithm is capable of adaptive learning in complex environments. Compared with traditional Artificial Potential Field (APF), Q-learning, and TD3 algorithms, PPO better balances exploration and exploitation and demonstrates stronger learning stability and global optimization capability in dynamic multi-obstacle scenarios. Simulation results show that PPO-based UAV path planning outperforms Q-learning and other comparative algorithms in terms of tracking accuracy, convergence speed, and robustness. In specific scenarios, Q-learning achieves a trajectory error of approximately 1 m, TD3 and APF exhibit errors around 0.3 m with noticeable oscillations, and PPO achieves an error of about 0.2 m. The UAV can follow the vehicle trajectory smoothly, with a more continuous path and rapidly converging, stable error curves, indicating the promising application potential of PPO in intelligent UAV control. The PPO-based UAV-tracking path planning method effectively enhances the UAV’s intelligent decision-making and path optimization capabilities, providing new technical approaches and a research foundation for intelligent UAV traffic and cooperative control systems. Full article

Collaborative operation of heterogeneous UAV swarms in dense urban environments remains challenging because right-of-way allocation is often rigid, frequent replanning consumes considerable onboard computation, and paths obtained by purely mathematical optimization may not be easy to execute under real dynamic constraints. This paper presents a physics-informed, event-triggered path planning and control framework, termed Physics-Informed SSA-MPC. Its global search layer is built on the Sparrow Search Algorithm (SSA), whose search mechanism originates from sparrow foraging and anti-predatory behaviors. On this basis, the method combines an event-triggered Stackelberg game for airspace coordination, a physically constrained SSA for global path generation, and an event-triggered MPC for local replanning. Battery State of Health (SoH) is incorporated into the adaptive search process, while Lévy-flight updates are limited by the maximum available acceleration to avoid infeasible path mutations. Local replanning is activated only when predicted safety ellipsoids overlap or tracking errors exceed prescribed thresholds, which helps reduce redundant computation. Simulations in a digital twin of Lujiazui, Shanghai, show that the proposed method shortens path length by 3.3% to 14.9%, reduces obstacle-avoidance latency to 45 ms, and achieves a 100% engineering feasibility rate. Full article

Addressing high dynamics, stringent non-holonomic constraints, and limited onboard computation in cooperative online trajectory planning for multiple fixed-wing UAVs in complex 3D obstacle environments, this paper proposes a Cooperative-3D-Quick-Dubins-RRT*. First, an offline motion-primitive database is engineered to align with RRT* mechanics: an unconstrained expansion mode facilitates rapid space exploration, while a constrained rewiring mode ensures kinodynamic continuity. This architecture, synergized with four targeted acceleration strategies (dimensionality reduction, elliptical sampling, tree pruning, and pre-discretized collision checking), significantly accelerates convergence. Second, a Dubins-detour-based time-coordination mechanism is designed to map cooperative timing constraints into controllable path-length adjustments, and the feasible adjustment range is analyzed to ensure realizability. Finally, simulations and hardware-in-the-loop experiments across a variety of representative scenarios are conducted for validation. The results show that, compared with the classical Dubins-RRT*, the proposed method achieves clear advantages in planning time and path length, demonstrating its suitability for online cooperative obstacle-avoidance planning of multiple UAVs. Full article

Underwater dynamic target detection, classification, localization, and tracking (DCLT) is central to maritime surveillance and monitoring and increasingly relies on distributed AUV-based robotic sonar networks operating in passive listening and, when required, cooperative multistatic modes. Achieving a robust performance in realistic oceans remains challenging, because sensor placement must adapt to time-varying acoustic conditions and target priors while preserving acoustic communication connectivity, and because frequent reconfiguration under dynamic currents makes classical large-scale planning computationally expensive. This paper presents an integrated deep reinforcement learning (DRL)-based framework for passive-stage sonar placement and dynamic reconfiguration in distributed AUV networks. First, we cast placement as a constructive finite-horizon Markov decision process (MDP) and train a Proximal Policy Optimization (PPO) agent to sequentially build a collision-free layout on a discretized surveillance grid. The terminal reward is formulated to jointly optimize the environment-aware detection performance, computed from BELLHOP-based transmission loss models, and global network connectivity, quantified using algebraic connectivity. Second, to enable time-critical reconfiguration, we estimate flow-aware motion costs for all AUV–destination pairs using a PPO with a Long Short-Term Memory (LSTM) trajectory policy trained for partial observability. The learned policy can be deployed onboard, allowing each AUV to refine its path online using locally sensed currents, improving robustness to ocean-model uncertainty. The resulting cost matrix is solved via an efficient zero-element assignment method to obtain the optimal one-to-one reassignment. In the reported simulation studies, the proposed Sequential PPO placement method achieves a final reward 16–21% higher than Particle Swarm Optimization (PSO) and 2–3.7% higher than the Genetic Algorithm (GA), while the proposed PPO + LSTM planner reduces average travel time by 30.44% compared with A*. The proposed closed-loop architecture supports frequent re-optimization, scalable fleet operation, and a seamless transition to communication-supported cooperative multistatic tracking after detection, enabling efficient, adaptive DCLT in dynamic marine environments. Full article

To address the key challenges in fire evacuation path planning, such as the tendency to converge to local optima, unbalanced computational efficiency, and suboptimal path quality, this study proposes the enhanced Hiking Optimization Algorithm of Differentiated Weighted Dynamic (WDHOA). The WDHOA integrates a three-phase cooperative framework, incorporating dynamic grouping, hybrid search, and angle generation. Comprehensive evaluations on the CEC 2017 and CEC 2022 benchmark suites demonstrate that WDHOA significantly outperforms eight widely used algorithms, such as LSHADE, RIME, SCA in convergence accuracy, stability, and robustness, especially for high-dimensional and multimodal functions. Wilcoxon rank-sum tests and Friedman tests confirm statistical significance across most functions. Ablation experiment further verifies the effectiveness of the three enhanced strategies. When applied to fire evacuation path planning, WDHOA achieves the best solutions while satisfying all nonlinear constraints. These experiments confirm that WDHOA effectively balance optimization accuracy and practical applicability in fire evacuation path planning problems. Full article