Search Results (359)

Nano-aerial vehicles have emerged as pivotal tools in modern robotics research, offering a safe and scalable means to validate complex algorithms in resource-constrained environments. This scoping review synthesizes the extensive body of work on the Crazyflie nano-quadcopter and evaluates its potential for drone application development in research and academia. The Crazyflie quadcopter has emerged as a leading open-source platform for education and research in aerial robotics due to its modularity and low cost. Despite its rapid evolution, there is currently no comprehensive synthesis mapping its diverse applications across hardware configurations and research domains. This evaluation systematically charts existing research on the Crazyflie platform, outlining its development, identifying relevant hardware and software configurations, categorizing major research topics, and identifying knowledge gaps. A systematic search was performed on three major databases, Scopus, Web of Science and Google Scholar, for studies published between 2015 and 2025. The results indicate a rapid growth in scientific production, an involved research community and very diverse thematic approaches. Expansion decks for the Crazyflie have been analyzed together with their relation to specific fields of research. While control systems remain the primary research theme, there is a significant shift toward artificial intelligence and swarm robotics. Full article

The multirotor configuration unmanned aerial vehicle faces a significant challenge in simultaneously achieving long-range operation and high payload capacity. This paper investigates the power management strategy for a novel fuel–electric hybrid aircraft that incorporates lifting wings to reduce rotor load and a range-extend system to enhance energy supply. An equivalent consumption minimization strategy is developed to optimize, in real time, the power distribution between the internal combustion engine and the battery. The primary innovation of this paper lies in the application and rigorous validation of the equivalent consumption minimization strategy on this new aircraft configuration, which effectively minimizes total energy cost by optimally balancing fuel consumption and battery degradation, resulting in significantly reduced fuel usage and a more stable power output compared to conventional approaches. Full article

To enhance the propulsion efficiency of near-space high-altitude unmanned aerial vehicle under low-density conditions and to gain a deeper understanding of the aerodynamic characteristics of contra-rotating propellers under complex interference, this study focuses on a high-altitude contra-rotating propeller propulsion system. A systematic investigation is conducted on the influence of design variables and flow characteristics. Considering the distinctive features of high-altitude environments, including low Reynolds numbers, high induced velocity ratios, and strong mutual interference between front and rear rotors, a numerical simulation method for contra-rotating propellers is established. The aerodynamic performance and typical flow structures are analyzed and compared with conventional propeller configurations to elucidate the aerodynamic advantages of contra-rotating propellers. Furthermore, key design variables such as axial distance, pitch angles of the front and rear propellers, and rotational speed matching are systematically examined to assess their effects on aerodynamic characteristics. Comparative analysis of axial velocity distributions reveals the interaction mechanisms between front and rear rotors under different parameter combinations and identifies the dominant factors influencing aerodynamic performance. The results indicate that rational matching of geometric parameters between front and rear rotors can effectively mitigate adverse interference, optimize wake structures, and improve the overall aerodynamic performance of contra-rotating propellers at high altitudes. These findings provide theoretical guidance and engineering references for the aerodynamic design and parameter selection of high-altitude contra-rotating propeller systems. Full article

Dragonflies exhibit remarkable flight stability in unsteady environments, largely due to aerodynamic interaction between their forewings and hindwings. This study investigates gust response in dragonfly-inspired micro-aerial vehicles (MAVs) from a system dynamics perspective, with emphasis on the aerodynamic role of tandem-wing interaction rather than control compensation. A six-degree-of-freedom (6DOF) rigid-body framework is developed and coupled with a quasi-steady aerodynamic model that includes explicit phase-dependent interaction between forewing and hindwing forces. Gusts are introduced as time-varying inflow perturbations, allowing physically consistent analysis of how disturbances propagate through aerodynamic loading into vehicle motion. Simulations are performed for representative flight conditions, including gliding, hovering, and gust-perturbed ascent. The results show bounded trajectory, velocity, and attitude responses under sustained gust excitation, even with conservative baseline control. Force and energy analyses indicate that wing–wake interaction redistributes aerodynamic loads in time and reduces peak force and moment fluctuations before they reach the rigid-body dynamics. This behavior is interpreted as passive aerodynamic filtering of gust disturbances inherent to the tandem-wing configuration. Comparative simulations using backstepping control and Active Disturbance Rejection Control (ADRC) further show that the dominant gust attenuation arises from aerodynamic configuration rather than from control action. Although the aerodynamic model is quasi-steady, the framework reproduces key trends reported in biological and CFD-based studies and provides a numerical foundation for future wind-tunnel and free-flight experiments on configuration-level gust attenuation. Full article

Aerial docking is a crucial capability for extending the autonomy and functionality of uncrewed aerial vehicles (UAVs), yet practical and robust docking mechanisms remain underdeveloped. Mid-air recovery also enables flexible multi-UAV cooperation across diverse mission scenarios. To address the core challenge of achieving reliable and precise airborne rendezvous, this paper proposes a control-driven approach supported by a complementary mechanical design. A Nonlinear Model Predictive Control (NMPC) framework is developed for the follower UAV, incorporating a velocity-penalty strategy to ensure the smooth and accurate tracking of the leader UAV based on GNSS guidance during the rendezvous phase. In the terminal docking stage, alignment accuracy is further enhanced through vision-based pose estimation using an ArUco marker array mounted on the leader UAV. Building on these algorithmic components, an improved active V-shaped docking mechanism is introduced to compensate for the follower UAV’s pitch angle during engagement, providing robustness against residual alignment errors. The feasibility and performance of the proposed system are validated through static ground docking experiments of the mechanical module and AirSim dynamic simulations evaluating the autonomous docking controller. Full article

Fixed-wing unmanned aerial vehicles, with their advantages of long endurance and substantial payload capacity, are poised to be a key platform for the future low-altitude economy. However, the challenge of achieving precise attitude tracking control under unknown time-varying disturbances persists. To tackle this difficulty, this article introduces a soft-sign function-based active disturbance rejection control (SSADRC) method, and develops a hybrid grey wolf optimizer (HGWO) with balanced exploration–exploitation mechanisms for intelligent parameter tuning. Specifically, SSADRC utilizes a novel smooth nonlinear function with saturation constraints to reconstruct the nonlinear feedback controller and the extended state observer, ensuring smooth and stable control output. Subsequently, HGWO integrates the good point set-based initialization strategy, the fitness-based dynamic-weight strategy, the diversity-based adaptive-mutation strategy, and the logistic chaotic map-based survival-of-the-fittest strategy, addressing the tuning of multiple coupled parameters in SSADRC. Additionally, the SSADRC-based pitch attitude controller is designed for a fixed-wing unmanned aerial vehicle, and an HGWO and seven other swarm optimization algorithms are employed to tune the parameters. The results demonstrate that the HGWO exhibits the best convergence accuracy in the SSADRC parameter optimization task, and SSADRC illustrates better command tracking performance and state estimation accuracy than typical ADRC. Full article

Various types of turbine engines have been chosen as the primary power source of the long-endurance unmanned aerial vehicles (UAVs) because of their high propulsive efficiency and low specific fuel consumption. To ensure the healthy operation of UAV turbine engines, rotor unbalance should be monitored and constrained to a preset limit. This paper proposes an efficient and physically interpretable method to achieve rotor unbalance monitoring. This method enables the frequency response function (FRF) to inform the neural network design, bringing the physics-informed convolutional neural network (PICNN). Firstly, the FRF gives a qualitative judgment of the axial positions of dominant faulty parts. Then, the following subnet proceeds to achieve quantitative identification. This method is demonstrated on a series of numerical cases and on a twin-disk rotor-bearing-casing experimental setup with anisotropic supporting stiffness. This setup is representative of engine installation status on the UAV platform. The results show that the PICNN can achieve higher precision compared to pure data-driven or model-based benchmarks. The PI layer does not require a high-fidelity model that generates responses identical to the actual ones. The robustness against modeling errors in stiffness and damping ratios is demonstrated. The achieved relative errors are less than 1.5% under various experimental datasets. Full article

Ice accretion is a significant threat to the operation of UAVs in cold climates. This study analyzed the performance degradation caused by ice accretion on a propeller with a diameter of 0.53 m for a small UAV in an icing wind tunnel. Three different droplet diameters of 20, 40, and 60 µm were tested along with three liquid water contents between 0.28 g/m³ and 1.12 g/m³ along with temperatures of −5 °C, −10 °C, and −15 °C. Additionally, the influence of the variation in the rotation rate was measured. The droplet diameter was observed to have the strongest influence on the propeller’s performance. An increase in the median volume diameter from 20 µm to 40 µm was correlated with a significant decrease in the propeller’s performance. After a minute of icing, the experiment at 20 µm showed a reduction in thrust of 25% compared to a decrease in thrust by 100% for the 40 µm case and 120% for the 60 µm case, meaning that the propeller is not generating thrust, but is generating drag. The temperature influences the propeller’s performance, with the most substantial performance degradation at −5 °C and a decrease in the performance impact with a temperature reduction. Analyzing the performance impact is an important step for deploying UAVs in icing conditions by detecting the most critical conditions for the performance of a UAV propeller. The analysis shows that the most critical conditions are at −5 °C and that an increase in droplet diameter and liquid water content leads to more severe icing conditions. The results show the need for future analysis comparing the performance impact of a propeller at different icing wind tunnels and the validation of numerical methods for predicting the performance degradation of a UAV propeller. Full article

Following the growing interest in small-scale unmanned aerial vehicles (UAVs), this paper presents a comprehensive conceptual design methodology for a modular ducted-fan aerial vehicle intended for research applications. Although ducted-fan configurations offer significant advantages over conventional multirotor platforms, particularly in urban, indoor, and human-interaction scenarios, the availability of affordable and customizable ducted-fan UAVs platforms suitable for scientific research remains limited. To address this gap, the paper details the complete design of the vehicle, including propeller aerodynamics and duct design, mechanical structure, actuation system, dynamic modeling, and control strategy. All major structural and aerodynamic components are fabricated using low-cost additive manufacturing, enabling rapid prototyping and high modularity. The vehicle’s performance is experimentally assessed through bench tests and indoor flight experiments, demonstrating stable flight and satisfactory attitude control. The presented work shows that a fully functional ducted-fan UAVs can be realized using commercial off-the-shelf electronics and exclusively 3D-printed components, and provides practical guidelines to replicate and adapt the proposed platform for advanced research in UAVs control, navigation, and autonomy. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in hazardous and dynamic environments, where path planning requires balancing competing objectives beyond simple distance minimisation. Classical planners such as Dijkstra, A*, and RRT* generate paths efficiently but often overlook mission-specific trade-offs involving energy use, risk avoidance, and reward maximisation. This work proposes a unified evaluation framework that integrates grid-based (Dijkstra, A*, weighted A*) and sampling-based (RRT, CARRT*) planners within parameterised environments embedding a range of functions into penalty and reward zones. A global cost function, $J=\alpha L+\beta E+\gamma P-\delta R,$ is applied post hoc to decouple path generation from mission prioritisation, enabling rapid reassessment under changing objectives such as low-fuel, high-safety, or speed-priority scenarios. Experiments conducted on an Apple M2 CPU, repeated three times per configuration to ensure statistical robustness, demonstrate that CARRT* achieves the lowest mission costs and highest efficiency for fuel- and time-sensitive missions, while deterministic grid-based planners perform better in safety- and reward-oriented contexts in four environments. These findings indicate that optimal UAV path planning depends not only on algorithmic efficiency but also on aligning planner choice with mission priorities. The framework provides a reproducible methodology for benchmarking and deploying mission-aware path planning strategies in research and operational settings. Full article

Flapping-wing micro air vehicles (FWMAVs) have gained increasing attention due to their manoeuvrability, low acoustic signature, and suitability for confined or cluttered environments. Despite considerable progress, existing reviews treat actuation mechanisms and mechanical transmissions in isolation, leaving a gap in the comparative assessment of pulley-based and alternative flapping systems. This study provides a comprehensive and quantitative synthesis of the literature on FWMAV mechanical architectures, with particular emphasis on pulley-driven transmissions used in platforms such as the Nano Hummingbird and the Robotic Hummingbird. A structured review methodology was applied, incorporating a systematic database search, extraction of performance parameters, and cross-platform comparison of flapping frequency, lift generation, power consumption, lift-to-weight ratio, and material choices. The analysis identifies consistent scaling trends across motor-driven, piezoelectric, and hybrid actuation families and highlights the efficiency and stroke-amplification advantages of pulley-based mechanisms for centimetre-scale hovering MAVs. The review also identifies unresolved challenges, including durability of transmission materials, standardisation of performance metrics, and the need for high-fidelity aerodynamic characterisation. Overall, this work offers an integrated framework for understanding the trade-offs among actuation and transmission strategies and provides a roadmap to guide future research and the practical development of next-generation FWMAVs. Full article

An online off-policy asynchronous real-time model reference tracking control (OOART-MRTC) algorithm is proposed and validated for unmanned aerial vehicles (UAVs) characterized by faulty actuation and parametric uncertainty. The optimal control problem is posed based on approximate dynamic programming (ADP) and reinforcement learning (RL) theory, using a virtual state-space representation constructed exclusively on input–output true system data, which exploits the observability theory. OOART-MRTC learns control by interacting with the system, starting from an initial stabilizing controller derived from an approximate uncertain model. Learning convergence and stability under the proposed adaptive behavior are analyzed. Since the learning iterations cannot update within a sampling period, an asynchronous mechanism is proposed for updating the controller parameters, leveraging real-time control and multi-tasking. The complexity associated with the resulting high-dimensional system is solved by efficient linear parameterization and validated on a realistic case study where three coupled double integrators describe the UAV attitude control. Full article

In this paper, we present a physical interaction scheme for omnidirectional multirotor aerial vehicles (MRAVs) equipped with fixedly tilted non-coplanar propellers, based on an admittance control architecture. An external wrench observer is employed to estimate the interaction wrench at the end-effector, hence eliminating the need for an additional force/torque sensor. We show that using the nominal allocation matrix in this class of admittance controllers can lead to a contact loss during complex interaction scenarios due to unmodeled and state-dependent aerodynamics effects. To address this issue, we propose a method for identifying the wrench map across different regions of the vehicle’s orientation in $SO\left(3\right)$ using free-flight experimental data. This is achieved by formulating a Quadratic Programming (QP) optimization whose solution provides the best approximation of the wrench map for a given orientation of the MRAV. The effectiveness of this approach is experimentally demonstrated, including static point contacts at various orientations, sliding contact, and peg-in-hole tasks. Full article

High-altitude low-speed aerostats are ideal unmanned platforms for communication coverage, remote sensing, environmental monitoring, aviation support, and other applications. To address practical operational needs such as rapid emergency deployment, this paper proposes a path planning method for low-speed aerostats based on the Markov decision process (MDP). The method is optimized to minimize deployment time while accounting for discrepancies between forecasted and actual wind fields. An uncertain wind field model is established to incorporate wind-related uncertainties into the MDP framework, with key parameters—including the state space, action set, immediate reward, and transition probability—designed accordingly. A mathematical model is formulated to address the global path planning problem under complex constraints, such as horizontal wind resistance capability, altitude control capacity, and flight time requirements. Simulation results demonstrate that the proposed method enables aerostats to achieve optimal 2D and 3D path planning under complex constraints. Furthermore, regional reachability is quantitatively analyzed, providing technical support for the rapid deployment of aerostats to target areas in practical applications. The core innovations of this work lie in the integration of a probabilistic wind uncertainty model with a constraint-aware MDP framework, enabling optimal 3D path planning and quantitative reachability analysis for high-altitude low-speed aerostats. Full article

This paper presents a robust approach for achieving accurate indoor positioning and autonomous navigation of quadcopters through the fusion of multiple radars and an inertial measurement unit (IMU) named hybrid-filtered Radar–Inertial Odometry (Hybrid-RIO). The Hybrid-RIO system integrates four-directional Frequency-Modulated Continuous-Wave (FMCW) millimeter-wave (mmWave) radars simultaneously with a high-performance IMU to continuously estimate the quadcopters’ position, velocity, and orientation even in low-light and indoor environments. The autonomous flight commands from the system further enable indoor navigation without requiring human intervention. Experimental results reveal notable advancements in both the accuracy and consistency of positioning. The integration of the proposed Hybrid-RIO approach holds promise in a wide spectrum of domains, including cave exploration, tunnel rescue operations, and indoor navigation solutions. Full article