Autonomous Flight Research Articles

The Problematic Issues of Unmanned Aerial Vehicle Application in Agriculture

The current rapid development of unmanned aviation is largely driven by the needs of the defense-industrial complex. In light of these events, the Russian industry has currently focused its attention on the development of unmanned aerial vehicles characterized by high payload capacity, autonomous flight range, and navigation precision. Considering the projected increase in the production rates of UAVs and the expansion of their functionalities, there is a pressing need for their active integration into various sectors of the economy, including agriculture. Despite the fact that the application of aerial agro-drones, like other aircraft, is regulated by the "Federal Rules for the Use of Airspace of the Russian Federation" (Government Resolution No. 138 dated March 11, 2010), there are currently no unified regulated approaches and requirements for the operation of unmanned aerial vehicles in agriculture. Given the growing relevance of issues related to the integration of unmanned aerial vehicles into agriculture, there is an increased need for the development of documents that regulate the use of agrochemicals for the treatment of agricultural land using these devices. The research objective if the development of a protocol for the inclusion of pesticides if the catalog approved for use through unmanned aerial vehicles.

L1 Adaptive Control for Small-Scale Unmanned Helicopters: Enhancing Speed Regulation

This paper focuses on the application of L1 adaptive control to the speed autopilot loop of small-scale unmanned helicopters. The L1 adaptive control technique is investigated for its potential to provide reliable speed control, particularly in handling external disturbances and model uncertainties. Although L1 adaptive control has been widely studied, its application to small-scale unmanned helicopters remains relatively unexplored. Through numerical simulations and a preliminary experimental test campaign conducted on a small rotary-wing platform, this paper contributes to validating L1 adaptive control as a promising solution for speed regulation in unmanned helicopter platforms.

Hovering Control on a Variable-Parameters Model of a Small Unmanned Helicopter Based on a Backstepping Sliding Mode Method

In order to strengthen the hovering precision of a small unmanned helicopter with a manipulator for completing a hovering operation, in this study, a double-loop control strategy of position and attitude is designed. In the position loop, a sliding mode controller is proposed. It attains high precision and strong robustness under continuous disturbance. With the manipulator continuously operating during helicopter hovering, the overall center of gravity and the moment of inertia of the system will also change. Therefore, a backstepping sliding mode controller is proposed in the attitude loop for controlling the hovering attitude of the helicopter. The high precision and strong robustness of the control system are proven. The digital simulation presents a position steady-state error of less than 2% under constant disturbance, and the hovering attitude angle fluctuation amplitude of the helicopter is less than 0.05 rad.

A Hybrid ARO Algorithm and Key Point Retention Strategy Trajectory Optimization for UAV Path Planning

Path planning is a fundamental research issue for enabling autonomous flight in unmanned aerial vehicles (UAVs). An effective path planning algorithm can greatly improve the operational efficiency of UAVs in complex environments like urban and mountainous areas, thus offering more extensive coverage for various tasks. However, existing path planning algorithms often encounter problems such as high computational costs and a tendency to become trapped in local optima in complex 3D environments with multiple constraints. To tackle these problems, this paper introduces a hybrid multi-strategy artificial rabbits optimization (HARO) for efficient and stable UAV path planning in complex environments. To realistically simulate complex scenarios, we introduce spherical and cylindrical obstacle models. The HARO algorithm balances exploration and exploitation phases using a dual exploration switching strategy and a population migration memory mechanism, enhancing search performance and avoiding local optima. Additionally, a key point retention trajectory optimization strategy is proposed to reduce redundant path points, thus lowering flight costs. Experimental results confirm the HARO algorithm’s superior search performance, planning more efficient and stable paths in complex environments. The key point retention strategy effectively reduces flight costs during trajectory optimization, thereby enhancing adaptability.

Pitch angle and altitude control for unmanned helicopter based on new approximation-free control.

This article introduces an enhanced non-approximated control technique for the pitch and altitude control systems of unmanned helicopters. It takes into account unpredictable external disturbances and system dynamics. The integration of prescribed performance control into unmanned helicopter systems significantly improves the transient and steady-state response capabilities. This approach avoids the computational complexities often associated with neural networks and fuzzy control methods. By avoiding the need for function approximation, which can introduce inaccuracies and computational overhead, the controller design process is streamlined. This method's simplicity and ability to handle unknown disturbances make it highly suitable for real-world implementation, where robustness and efficiency are paramount. Finally, simulations are conducted to showcase the improved transient and steady-state response capabilities achieved by the proposed approach.

Multi-objective Route Planning of an Unmanned Air Vehicle in Continuous Terrain: An Exact and an Approximation Algorithm

Unmanned Aerial Vehicles (UAVs) are widely used for military and civilian purposes. Effective route planning is an important component of their successful missions. In this study, we address the route planning problem of a UAV tasked with collecting information from various target locations in a protected terrain. We consider multiple targets, three objectives, and time-dependent information availability. Modeling the movement of UAVs in a continuous terrain in the presence of multiple objectives is complex. Conflicting objectives typically lead to a continuum of efficient trajectory options between two targets. We formulate the routing problem as a mixed-integer programming (MIP) model that captures the movement in the continuous terrain. We demonstrate the superiority of the continuous terrain formulation over the simplified discretized terrain formulation. We also develop an approximation algorithm that reduces the computational requirements of the MIP model substantially while ensuring a desired level of precision.

Retraction Note: An agent architecture for autonomous UAV flight control in object classification and recognition missions

Retraction Note: An agent architecture for autonomous UAV flight control in object classification and recognition missions

Autonomous Landing Strategy for Micro-UAV with Mirrored Field-of-View Expansion.

Positioning and autonomous landing are key technologies for implementing autonomous flight missions across various fields in unmanned aerial vehicle (UAV) systems. This research proposes a visual positioning method based on mirrored field-of-view expansion, providing a visual-based autonomous landing strategy for quadrotor micro-UAVs (MAVs). The forward-facing camera of the MAV obtains a top view through a view transformation lens while retaining the original forward view. Subsequently, the MAV camera captures the ground landing markers in real-time, and the pose of the MAV camera relative to the landing marker is obtained through a virtual-real image conversion technique and the R-PnP pose estimation algorithm. Then, using a camera-IMU external parameter calibration method, the pose transformation relationship between the UAV camera and the MAV body IMU is determined, thereby obtaining the position of the landing marker's center point relative to the MAV's body coordinate system. Finally, the ground station sends guidance commands to the UAV based on the position information to execute the autonomous landing task. The indoor and outdoor landing experiments with the DJI Tello MAV demonstrate that the proposed forward-facing camera mirrored field-of-view expansion method and landing marker detection and guidance algorithm successfully enable autonomous landing with an average accuracy of 0.06 m. The results show that this strategy meets the high-precision landing requirements of MAVs.

An estimation method for vision-based autonomous landing system for fixed wing aircraft

In this paper, we propose a vision-based estimation method for autonomous landing of fixed-wing aircraft for Advanced Air Mobility. The concept of autonomous flight with minimal human intervention has gained significant interest with a particular focus on autonomous landing. The goal is to overcome the limitations of existing instrument landing systems and reduce reliance on GNSS during aircraft landing. The proposed system leverages the following techniques for high-performance and real-time operations in various real-world environments and during all stages of landing. A deep learning segmentation model was directly learned, created, and used for robust runway recognition performance against environmental changes around the runway. Algorithms were designed to adapt to different flight stages considering the varying information offered by image data when the aircraft is in mid-air and on the ground. The relative lateral position from the aircraft to the runway was estimated using bird’s-eye-view conversion and inverse projection techniques instead of conventional perspective-n-point methods for reducing the recognition error occurring from the conversion between 2D image and 3D world. Finally, the mixed-precision technique was used for the segmentation model to improve inference speed and ensure real-time operation in real-world deployment. Estimation stability and reliability were improved by using a Kalman Filter to cope with sensor uncertainties caused by the real-world flight environment. Consequently, we show that the average error in the lateral position estimation by the proposed method is comparable to the accuracy of a single GNSS and demonstrate successful autonomous landing of our test aircraft with a set of flight tests.

Digital Traffic Lights: UAS Collision Avoidance Strategy for Advanced Air Mobility Services

With the advancing development of Advanced Air Mobility (AAM), there is a collaborative effort to increase safety in the airspace. AAM is an advancing field of aviation that aims to contribute to the safe transportation of goods and people using aerial vehicles. When aerial vehicles are operating in high-density locations such as urban areas, it can become crucial to incorporate collision avoidance systems. Currently, there are available pilot advisory systems such as Traffic Collision and Avoidance Systems (TCAS) providing assistance to manned aircraft, although there are currently no collision avoidance systems for autonomous flights. Standards Organizations such as the Institute of Electrical and Electronics Engineers (IEEE), Radio Technical Commission for Aeronautics (RTCA), and General Aviation Manufacturers Association (GAMA) are working to develop cooperative autonomous flights using UAS-to-UAS Communication in structured and unstructured airspaces. This paper presents a new approach for collision avoidance strategies within structured airspace known as “digital traffic lights”. The digital traffic lights are deployed over an area of land, controlling all UAVs that enter a potential collision zone and providing specific directions to mitigate a collision in the airspace. This strategy is proven through the results demonstrated through simulation in a Cesium Environment. With the deployment of the system, collision avoidance can be achieved for autonomous flights in all airspaces.

A Butterfly Algorithm That Combines Chaos Mapping and Fused Particle Swarm Optimization for UAV Path Planning

Effective path planning is essential for autonomous drone flight to enhance task efficiency. Many researchers have applied swarm intelligence algorithms to drone path planning. For instance, the traditional Butterfly Optimization Algorithm (BOA) has been used for this purpose. However, traditional BOA faces challenges such as slow convergence and susceptibility to being trapped in local optima. An Improved Butterfly Optimization Algorithm (IBOA) has been developed to identify optimal routes to address these limitations. Initially, ICMIC mapping is utilized to establish the butterfly community, enhancing the initial population’s diversity and preventing premature algorithm convergence. Following this, a population reset strategy is introduced, replacing weaker individuals over a specified number of iterations while maintaining a constant population size. This strategy enhances the algorithm’s ability to avoid local optima and increases its robustness. Additionally, characteristics of the Particle Swarm Optimization (PSO) algorithm are integrated to enhance the butterfly’s location update mechanism, accelerating the algorithm’s convergence rate. To evaluate the performance of the IBOA algorithm, this study designed a CEC2020 function test experiment and compared it with several swarm intelligence algorithms. The results showed that IBOA achieved the best performance in 70% of the function tests, outperforming 75% of the other algorithms. In the path planning experiments within a simulated environment, IBOA quickly converged to the optimal path, and the paths it planned were the shortest and safest compared to those generated by other algorithms.

Design of Neural Network-Based Multi-Fault Tolerant Control System for Unmanned Aerial Vehicles

Actuator failure seriously threatens the flight safety of unmanned helicopters. Considering the problem of multiple faults such as actuator bias and failure in unmanned helicopters, a composite fault-tolerant flight control algorithm is proposed. For actuator bias fault, a nonlinear fault observer is designed to estimate it in real time; for actuator failure fault, a same-dimensional auxiliary system is constructed and processed by neural network technology. The composite fault-tolerant flight controller of the unmanned helicopter is designed by backstepping method, and the Lyapunov stability theory is used to prove that the error signals of the closed-loop system are bounded and convergent. Simulation results show that the proposed control algorithm can improve the fault tolerance of the unmanned helicopter when multiple actuator faults occur, ensuring its safe flight.

An integrated systematic method for interlaced unmanned spatial systems (IUSS) design process

Unmanned Aerial Systems (UAS) have garnered significant interest in recent years. These systems, commonly consisting of one or more Unmanned Aerial Vehicles (UAV) and satellite systems, have been extensively used to enhance the effectiveness of various SOSs, such as disaster management and relief efforts. In addition, the inaugural Mars unmanned helicopter, Ingenuity (Ginny), successfully took flight in April 2021, marking the beginning of the utilization of these systems on Mars. This research aims to build an integrated approach for developing Interlaced Unmanned Spatial Systems, considering the high level of precision required for space systems. This study aims to set up and optimize all parts of the proposed architecture for the design of IUSS, using Model-Based Systems Engineering theories and Dependency Structural Matrix foundations. This research introduces a comprehensive coherence architecture that considers all design domains, including the design process, design office, products, and requirements. Additionally, a design workflow model is provided.

Second-order DSC-based adaptive fuzzy tracking control for uncertain unmanned helicopter with fixed-time stability

Second-order DSC-based adaptive fuzzy tracking control for uncertain unmanned helicopter with fixed-time stability

Accuracy and Computational Cost Comparison of Numerical Flight Dynamics Reduced-Order Models

This paper compares several approaches to model the unsteady and nonlinear longitudinal aerodynamics of a generic unmanned combat air vehicle configuration based on computational fluid dynamics (CFD). Three different reduced-order models (ROMs) are implemented through unsteady simulations to predict the evolution of forces and moments during prescribed trajectories carried out at M=0.15. The first model investigates a linear quasi-steady model based on a first-order Taylor series expansion of the aerodynamic coefficients. The second one picks up the mathematical formulation of the first model but adds a dynamic coefficient in order to take into account the unsteady effect of rotation rate variations. Finally, the third model is built on indicial functions computed from positive step changes in the angle of attack or pitch rate. The unknown parameters of the first two models and the unknown indicial functions of the third one are obtained, respectively, from the study of forced oscillations and step motions. These prescribed trajectories are performed using unsteady Reynolds-averaged Navier–Stokes simulations coupled with a grid displacement tool. Two different methods are tested and compared to carry out the CFD mesh motions: an overset method and a full moving grid (FMG) method. The indicial functions obtained using the FMG method give results equivalent to those found in the literature in half the time of the overset method. To reduce the computational costs to set up the ROMs, the FMG method is preferred to the overset method. ROMs are then applied, and their predictions are compared with CFD simulations for prescribed trajectories with different angular rates and angles of attack. A study of instantaneous and overall accuracy associated with the implementation cost of the ROMs has allowed to show their strengths and their weaknesses. Similar results are observed at low angles of attack for the ROMs studied, while a major modeling advantage of the indicial method has been identified for higher angles of attack in the nonlinear domain.

Shape considerations for the design of propellers with trailing edge serrations

Noise reductions due to trailing edge serrations of several representative unmanned air vehicle propellers are calculated using a low-order methodology based on RANS simulations coupled with an extension of Ayton’s model proposed by Li and Lee, which provides a heuristic three-dimensional model for finite span applicable to rotor blades. The latter model is validated in the limit of zero serration amplitude against Amiet’s and Schlinker and Amiet’s models, finding good agreement at high frequencies for both airfoils and rotating blade elements. Similar good validation results are obtained for finite serrations by comparing with experiments achieved on the Controlled Diffusion airfoil at Université de Sherbrooke, and with calculations for a serrated blade element by Tian and Lyu. The coupled methodology is then validated both aerodynamically and acoustically with ISAE measurements for a representative drone propeller at different rotational speeds. The corresponding serrated model is then used to calculate noise reductions caused by different shapes. The square wave serration is shown to outperform the sawtooth and sinusoidal shapes for all frequencies and observer angles for small propeller blades typically used for drones. Yet, for larger chord blades typically used for ducted fans, combinations of sawtooth and sinusoidal serrations provide better noise reductions.

Dynamic Double Event-Triggered Anti-Disturbance Tracking Control for a 2-DOF Small Unmanned Helicopter.

This article devises a new dynamic double event-triggered anti-disturbance tracking control scheme for a 2-degree of freedom (DOF) laboratory helicopter subject to external time-varying disturbances and load fluctuation by using the generalized proportional-integral observer technique. The helicopter system is separated into two subsystems in the proposed control method, i.e., the pitch subsystem and the yaw subsystem. Each subsystem includes a discrete-time dynamic double event-triggering mechanism (DDETM), and the control laws of the two subsystems are independent of each other. There are two triggering conditions in the designed double triggering mechanism: one is designed based on the system states and the other is based on the lumped disturbance estimation. These two triggering conditions form a competitive relationship such that the controller updates the control signal as long as one of the triggering conditions is satisfied. Theoretical analysis is provided for achieving the better communication and control performance of the proposed DDETM-based robust control method. Through rigorous stability analysis, it is proved that the closed-loop hybrid system is globally ultimately bounded. At last, numerical simulations show that the suggested control strategy not only reduces the event-triggering number, but also improves the initial dynamic performance of the system.

Enhancing air traffic control: A transparent deep reinforcement learning framework for autonomous conflict resolution

Rising traffic demands, advancements in automation, and communication breakthroughs are opening new design frontiers for future air traffic controllers (ATCs). This article introduces a deep reinforcement learning (DRL) controller to support conflict resolution in autonomous free flight. While DRL has made significant strides in this domain, there is scant focus in existing research on the explainability and safety of DRL controllers, especially their robustness against adversarial attacks. To tackle these concerns, we have engineered a fully transparent DRL framework that: (i) separates the intertwined Q-value learning model into distinct modules for safety-awareness and efficiency (target attainment); and (ii) incorporates data from nearby intruders directly, thereby obviating the need for central control. Our simulated experiments demonstrate that this bifurcation of safety-awareness and efficiency not only enhances performance in free flight control tasks but also substantially boosts practical explainability. Moreover, the safety-oriented Q-learning component offers detailed insights into environmental risk factors. To assess resilience against adversarial attacks, we present a novel attack strategy that induces both safety-centric and efficiency-centric disruptions. This adversary aims to degrade safety/efficiency by targeting the agent at select time intervals. Our experimental results reveal that this targeted approach can provoke as many collisions as a uniform attack strategy — that is, attacking at every opportunity — while engaging the agent four times less frequently, shedding light on the potential and limitations of DRL in future ATC system designs. The source code is available to the public at https://github.com/WLeiiiii/Gym-ATC-Attack-Project.

Modeling and control strategy of small unmanned helicopter rotation based on deep learning

Unmanned Aerial Vehicle (UAV) can serve as a substitute for workers in some hazardous environments, but the presence of ground effects makes UAVs prone to hardware damage during the recycling process. To study the rotation phenomenon of small UAV landing process and propose control strategy, the study completes the rotation modeling of small unmanned helicopter based on deep learning algorithm and proposes the control strategy. The results show that the CNN with ReLU function has the best performance, and the model converges in the 5th iteration with this function, while the model with Sigmoid function converges in the 36th iteration, and the fitting effect of the rotation model constructed by the study is higher than that of the traditional rotation model. The actual trajectory of the research-constructed rotation model starts to coincide with the expected trajectory at the 5th s of the landing process, while the actual trajectory of the Cheeseman-Bennett model starts to coincide with the expected trajectory only at the 26th s of the landing process. Under the control strategy model proposed in the study, the roll angle and pitch angle of UAV are stabilized at 46s, and the fluctuation of yaw angle is also minimal. The rotation model constructed in the study can completely reflect the rotation process of the small UAV, and the designed control system can help the UAV recover stability faster.

UAV path planning based on bird flock migration

Unmanned aerial vehicle, generally referred to as UAV, due to its high-performance and low-cost characteristics, it is particularly widely used in both military and civil applications, and is often used to perform a variety of complex missions. Trajectory planning is the basis for UAVs to realize autonomous flight, which ensures that UAVs avoid obstacles and threatening areas when performing tasks, and guarantees the safe execution of tasks. The operation of a UAV requires finding the safest and most efficient trajectory from a starting point to a specified end point based on several specific constraints. Current UAV trajectory planning algorithms, such as swarm algorithm, particle swarm algorithm, and ant colony algorithm, have been widely recognized, but still have many limitations when they are applied to complex and variable terrain. Therefore, this paper proposes an improved UAV trajectory planning algorithm based on simulated bird migration, and uses matlab for simulation and algorithm evaluation. The simulation results indicate that the improved search strategy is effective, the algorithm can find a better solution in each iteration, and the search strategy of the algorithm can effectively guide the algorithm towards a more optimized region. Overall, this algorithm has high research value in both military and civilian fields.