Search Results (2,867)

This paper proposes a robust control method of trajectory tracking for quadrotors under disturbance conditions, combining Model Predictive Control (MPC) and the Super-Twisting Algorithm (STA). MPC is a control strategy that solves an optimization problem by predicting the finite time future response from the model under control at each time step. However, MPC cannot guarantee control performance under disturbances such as modeling errors and wind gusts because it predicts future states of the control objects using a nominal model. To solve this problem, we propose a composite control method that uses Adaptive Super-Twisting Sliding Mode Disturbance Observer (ASTSMDO), which constrains the system to follow the MPC’s nominal model. The effectiveness of the proposed method is confirmed through numerical simulation. Compared to conventional MPC, the proposed controller achieves superior robustness and trajectory tracking performance under modeling error and wind disturbance. Full article

This study proposes a hierarchical framework with task priority perception for mission planning, to enhance multi-UAV coordination in maritime emergency search and rescue. By establishing a hierarchical decoupling optimization mechanism, the complex multi-region coverage problem is decomposed into two stages: task allocation and path planning. First, a coverage voyage estimation model is constructed based on regional geometric features to provide basic data for subsequent task allocation. Second, an improved multi-objective, multi-population grey wolf optimizer (IM2GWO) is designed to solve the task allocation problem; this integrates adaptive genetic operations and the multi-population coevolutionary mechanism. Finally, a globally optimal coverage path is generated based on the improved dynamic programming (DP). Simulation results indicate that the proposed method effectively reduces total task duration while boosting overall coverage benefits through the aggregation of high-value regions. IM2GWO demonstrates statistically superior performance with respect to the Pareto front distribution index across all test scenarios. Meanwhile, the path planning module based on DP can effectively reduce the overall coverage path cost. Full article

In disaster relief operations, integrating disaster reconnaissance, material delivery, and effect evaluation into a temporal task chain can significantly reduce emergency response cycles and improve rescue efficiency. However, since multiple types of heterogeneous UAVs need to be coordinated during the rescue temporal task chains assignment process, this places higher demands on the real-time dynamic decision-making and system fault tolerance of its task assignment algorithm. This study addresses the sequential dependencies among disaster reconnaissance, material delivery, and effect evaluation stages. A task allocation model for heterogeneous UAV swarm targeting temporal task chains is formulated, with objectives to minimize task completion time and energy consumption. A dynamic coalition formation algorithm based on temporary leader election and multi-round negotiation mechanisms is proposed to enhance continuous decision-making capabilities in complex disaster environments. A simulation scenario involving twenty heterogeneous UAVs and seven temporal rescue task chains is constructed. The results show that the proposed algorithm reduces average task completion time by 15.2–23.7% and average fuel consumption by 18.3–26.4% compared with cooperative network protocols and distributed auctions, with up to a 43% reduction in fuel consumption fluctuations. Full article

With the increasing deployment of Unmanned Aerial Vehicle (UAV) swarms in uncertain and dynamically changing environments, optimal cooperative control has become essential for ensuring robust and efficient system coordination. To this end, this paper designs a data-driven optimal bipartite containment tracking control scheme for multi-UAV systems under compound uncertainties. A novel Dynamic Iteration Regulation Strategy (DIRS) is proposed, which enables real-time adjustment of the learning iteration step according to the task-specific demands. Compared with conventional fixed-step data-driven algorithms, the DIRS provides greater flexibility and computational efficiency, allowing for better trade-offs between the performance and cost. First, the optimal bipartite containment tracking control problem is formulated, and the associated coupled Hamilton–Jacobi–Bellman (HJB) equations are established. Then, a data-driven iterative policy learning algorithm equipped with the DIRS is developed to solve the optimal control law online. The stability and convergence of the proposed control scheme are rigorously analyzed. Furthermore, the control law is approximated via the neural network framework without requiring full knowledge of the model. Finally, numerical simulations are provided to demonstrate the effectiveness and robustness of the proposed DIRS-based optimal containment tracking scheme for multi-UAV systems, which can reduce the number of iterations by 88.27% compared to that for the conventional algorithm. Full article

The electric propulsion system serves as the power source for multi-rotor drones, helping them complete various maneuvering actions. The reliability of this system directly affects whether the drone can successfully complete its mission. The multi-rotor drone propulsion system is a k-out-of-n system with functional dependence (FDEP). With the insufficient basis for selecting k-values, the problem of incalculable reliability caused by computational space explosion due to voting gates, and the uncertain impact of functional dependence on system reliability, we propose a reliability evaluation method based on controllability theory and BN (Bayesian network) reconstruction. The drone is dynamically modeled, and a control model is built, and k-values are selected through different failure combination controllability evaluations. We model the system with BN, use functional dependent components as BN node inputs, and reconstruct BN via an adder model to solve the problem of exponential growth in the conditional probability table. This paper analyzes system reliability, safety, and the impact of FDEP on the system, and conducts component importance analysis. The result provides important reference for the reliability, safety assessment, and dynamic maintenance processes of multi-rotor drone. Full article

The increasing demand for efficient aviation navigation system inspections has led to the use of Unmanned Aerial Vehicles (UAVs) as a flexible and cost-effective alternative to traditional manned aircraft. This study emphasizes the operational advantages of UAVs in transforming flight inspections, including Non-Directional Beacon (NDB) calibration. Following the successful performance evaluation of an NDB system in Banská Bystrica, Slovakia, using a manned aircraft, a UAV was deployed on the same flight path to validate its ability to replicate the procedure in terms of trajectory only, without performing any signal measurement. The UAV maintained accurate flight paths and continuous communication throughout the mission. A specialized rotatory system, operating at 868 MHz, enabled real-time tracking and ensured stable communication over long distances. The manned aircraft test revealed a maximum bearing deviation of 13.47° at 3.37 NM and a minimum received signal strength of −90 dBm, which approaches the ICAO threshold for en route navigation (±10°) but remains usable for diagnostic purposes. The UAV flight did not include signal capture but successfully completed the 40 NM profile with a circular error probable (CEP95) of 2.8 m and communication link uptime of 99.8%, confirming that the vehicle can meet procedural trajectory fidelity. These findings support the feasibility of UAV-based NDB inspections and provide the foundation for future test phases with onboard signal monitoring systems. Full article

This study introduces an innovative refinement to EKF-based monocular SLAM by incorporating attitude, altitude, and range-to-base data to enhance system observability and minimize drift. In particular, by utilizing a single range measurement relative to a fixed reference point, the method enables unmanned aerial vehicles (UAVs) to mitigate error accumulation, preserve map consistency, and operate reliably in environments without GPS. This integration facilitates sustained autonomous navigation with estimation error remaining bounded over extended trajectories. Theoretical validation is provided through a nonlinear observability analysis, highlighting the general benefits of integrating range data into the SLAM framework. The system’s performance is evaluated through both virtual experiments and real-world flight data. The real-data experiments confirm the practical relevance of the approach and its ability to improve estimation accuracy in realistic scenarios. Full article

An asymptotic stability velocity tracking controller is designed to enable the autonomous maneuvering flight of unmanned helicopters. Firstly, taking the UH-60A without pilots as the research object, a high-efficient rotor aerodynamic modeling is developed, which incorporates a free-wake vortex method with the flap response of blades. The consummate flight dynamic model is complemented by wind tunnel-validated fuselage/tail rotor load regressions. Secondly, a linear state–space equation is derived via the small perturbation linearization method based on the flight dynamic model within the body coordinate system. A decoupled model is formulated based on the linear state–space equation by employing the implicit model approach. Subsequently, a system of ordinary differential equations is constructed, which is related to the deviation between actual velocity and its expected value, along with higher-order derivatives of this discrepancy. The I&I (immersion and invariance) theory is then employed to facilitate the design of a non-cascade control loop. Finally, the response of desired velocity in longitudinal channel is simulated with step signal to compare the control effect with a PID (proportional–integral–derivative) controller. By adjusting the coefficients, the response progress of the PID controller is similar to the effect of adaptive controller with I&I theory. However, there is no obvious overshoot in the process with I&I adaptive controller, and the average response amplitude accounts for 16.69% of the random white noise, which is 7.38% of the oscillation level under the PID controller. The parameter tuning complexity when employing I&I theory is significantly lower than that of the PID controller, which is evaluated by mathematical derivations and simulations. Meanwhile, the sidestep and pirouette maneuvers are simulated and analyzed to examine the controller in accordance with the performance criteria outlined in the ADS-33E-REF standards. The simulation results demonstrate that the speed expectation-oriented asymptotic stability control can achieve a fast response. Both sidestep and pirouette maneuvers can satisfy the desired performance requirements stipulated by ADS-33E-REF. Full article

The locomotion control of unmanned quadruped robots has been one of the greatest challenges in robotics. Deep reinforcement learning has made great achievements in robot control. However, extracting effective features from historical information to improve locomotion agility is still an open and challenging problem. In this paper, a heterogeneous time-series soft actor–critic (HTS-SAC) method is proposed to enable better policy learning from historical data. Firstly, four mutual information decision conditions are developed for feature selection, which can analyze the correlation between input states and motion performance, obtaining the importance of temporal features of different lengths. Then, according to the results of feature optimization, a novel heterogeneous time-series neural network and the HTS-SAC locomotion control method are designed. Finally, the effectiveness of the proposed method is validated on different terrains using a Laikago quadruped robot simulation model. Full article

Unmanned aerial vehicles (UAVs) with high mobility and self-organization capabilities can establish highly connected networks to cache popular content for edge users, which improves network stability and significantly reduces access time. However, an uneven distribution of demand and storage capacity may reduce the utilization of the storage capacity of UAVs without a proper UAV coordination mechanism. This work proposes a multi-UAV-enabled caching socially aware network (SAN) where UAVs can switch roles by adjusting the social attributes, effectively enhancing data interaction within the UAVs. The proposed network breaks down communication barriers at the UAV layer and integrates the collective storage resources by incorporating social awareness mechanisms to mitigate these imbalances. Furthermore, we formulate a multi-objective optimization problem (MOOP) with the objectives of maximizing both the diversity of cached content and the total request probability (RP) of the network, while employing a multi-objective particle swarm optimization (MOPSO) algorithm with a mutation strategy to approximate the Pareto front. Finally, the impact of key parameters on the Pareto front is analyzed under various scenarios. Simulation results validate the benefits of leveraging social attributes for resource allocation and demonstrate the effectiveness and convergence of the proposed algorithm for the multi-UAV caching strategy. Full article

This study investigates the scheduling problem of return communication tasks for unmanned aerial vehicle (UAV) swarms, where disaster relief environmental global positioning is hampered. To characterize the utility of these tasks and optimize scheduling decisions, we developed a time window-constrained scheduling model that operates under constraints, including communication base station time windows, battery levels, and task uniqueness. To solve the above model, we propose an enhanced algorithm through integrating Dueling Deep Q-Network (Dueling DQN) into adaptive large neighborhood search (ALNS), referred to as Dueling DQN-ALNS. The Dueling DQN component develops a method to update strategy weights, while the action space defines the destruction and selection strategies for the ALNS scheduling solution across different time windows. Meanwhile, we design a two-stage algorithm framework consisting of centralized offline training and decentralized online scheduling. Compared to traditionally optimized search algorithms, the proposed algorithm could continuously and dynamically interact with the environment to acquire state information about the scheduling solution. The solution ability of Dueling DQN is 3.75% higher than that of the Ant Colony Optimization (ACO) algorithm, 5.9% higher than that of the basic ALNS algorithm, and 9.37% higher than that of the differential evolution algorithm (DE). This verified its efficiency and advantages in the scheduling problem of return communication tasks for UAVs. Full article

Autonomous UAV navigation in unknown and complex environments remains a core challenge, especially under limited sensing and computing resources. While most methods rely on modular pipelines involving mapping, planning, and control, they often suffer from poor real-time performance, limited adaptability, and high dependency on accurate environment models. Moreover, many deep-learning-based solutions either use RGB images prone to visual noise or optimize only a single objective. In contrast, this paper proposes a unified, model-free vision-based DRL framework that directly maps onboard depth images and UAV state information to continuous navigation commands through a single convolutional policy network. This end-to-end architecture eliminates the need for explicit mapping and modular coordination, significantly improving responsiveness and robustness. A novel multi-objective reward function is designed to jointly optimize path efficiency, safety, and energy consumption, enabling adaptive flight behavior in unknown complex environments. The trained policy demonstrates generalization in diverse simulated scenarios and transfers effectively to real-world UAV flights. Experiments show that our approach achieves stable navigation and low latency. Full article

Collaborative unmanned aerial vehicle (UAV) swarm operations using the open-source PX4–ROS2 system have been extensively studied for reconnaissance and autonomous missions. PX4–ROS2 utilizes data distribution service (DDS) middleware to ensure network flexibility and support scalable operations. DDS enables decentralized information exchange through its discovery protocol. However, in dense swarm environments, the default initialization process of this protocol generates considerable communication overhead, which hinders reliable peer detection among UAVs. This study introduces an optimized DDS discovery scheme incorporating two key strategies: a preloading method that embeds known participant data before deployment, and a dynamic network awareness approach that regulates discovery behavior based on real-time connectivity. Integrated into PX4–ROS2, the proposed scheme was assessed through both simulations and real-world testing. Results demonstrate that the optimized discovery process reduced peak packet traffic by over 90% during the initial exchange phase, thereby facilitating more stable and scalable swarm operations in wireless environments. Full article

Wind estimation plays a crucial role in atmospheric boundary layer research and aviation flight safety. Fixed-wing UAVs enable rapid and flexible detection across extensive boundary layer regions. Traditional meteorological fixed-wing UAVs require either additional wind measurement sensors or sustained turning maneuvers for wind estimation, increasing operational costs while inevitably reducing mission duration and coverage per flight. This paper proposes a real-time wind estimation method based on an Unscented Kalman Filter (UKF) without aerodynamic sensors. The approach utilizes only standard UAV avionics—GNSS, pitot tube, and Inertial Measurement Unit (IMU)—to estimate wind fields. To validate accuracy, the method was integrated into a meteorological UAV equipped with a wind vane sensor, followed by multiple flight tests. Comparison with wind vane measurements shows real-time wind speed errors below 1 m/s and wind direction errors within 20° (0.349 rad). Results demonstrate the algorithm’s effectiveness for real-time atmospheric boundary layer wind estimation using conventional fixed-wing UAVs. Full article

This paper proposes a finite-time adaptive reinforcement learning (RL) control law for a class of morphing unmanned aircraft with mismatched disturbances and coupled uncertainties. To handle the mismatched disturbances, an adaptive upper-bound estimator as well as the parameter adaptive laws have been proposed. Aiming at the coupled uncertainties, an RL-based online uncertainty estimator and a corresponding finite-time compensation control law have been developed. To deal with the non-affine characteristics, an auxiliary integral system has been introduced. By systematically integrating the aforementioned adaptive upper-bound estimators, finite-time control law, and the auxiliary signals, a novel RL-based adaptive finite-time control framework is constructed for morphing unmanned aircraft. Simulation results reveal the finite-time convergence and the advantages of the proposed method. Full article