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This paper presents the implementation of the Pure Pursuit control algorithm for path tracking on an agricultural pesticide spraying drone. Precise path tracking ensures accurate pesticide coverage, maximizing crop yield and minimizing environmental impact. Conventional position control architectures used by most agricultural drones can lead to inconsistent pesticide distribution due to variability in drone speed. Position control also causes deceleration and acceleration at corners, resulting in over-spraying in these areas. This lack of uniform spray distribution challenges efficient and sustainable agriculture. The Pure Pursuit algorithm is favored for its simplicity and effectiveness in autonomous navigation. The software architecture, including the flight control stack and ROS2-based PX4 simulation architecture, demonstrates the drone’s accurate trajectory following capabilities. Simulation tests evaluated the path tracking accuracy and overall performance of the system. Comparative results show the Pure Pursuit controller’s superiority over standard position controllers in terms of accuracy, robustness, and adaptability. Additionally, this paper introduces an innovative Coverage Path Planning (CPP) strategy based on grid decomposition. Integrated with the Pure Pursuit control mechanism, this CPP strategy ensures precise path tracking and maximizes coverage uniformity, further enhancing the effectiveness and sustainability of agricultural spraying operations.

Real-time (re-)planning is crucial for autonomous quadrotors to navigate in uncertain environments where obstacles may be detected and trajectory plans must be adjusted on-the-fly to avoid collision. In this paper, we present a control system design for autonomous quadrotors that has real-time re-planning capability, including the hardware pipeline for the hardware–software integration to realize the proposed real-time re-planning algorithm. The framework is based on a modified version of the PX4 Autopilot and a Raspberry Pi 5 companion computer. The planning algorithm utilizes minimum-snap trajectory generation, taking advantage of the differential flatness property of quadrotors, to realize computationally light, real-time re-planning using an onboard computer. We first verify the control system and the planning algorithm through simulation experiments, followed by implementing and demonstrating the system on hardware using a quadcopter. [ABSTRACT FROM AUTHOR]

This paper presents the implementation of the Pure Pursuit control algorithm for path tracking on an agricultural pesticide spraying drone. Precise path tracking ensures accurate pesticide coverage, maximizing crop yield and minimizing environmental impact. Conventional position control architectures used by most agricultural drones can lead to inconsistent pesticide distribution due to variability in drone speed. Position control also causes deceleration and acceleration at corners, resulting in over-spraying in these areas. This lack of uniform spray distribution challenges efficient and sustainable agriculture. The Pure Pursuit algorithm is favored for its simplicity and effectiveness in autonomous navigation. The software architecture, including the flight control stack and ROS2-based PX4 simulation architecture, demonstrates the drone's accurate trajectory following capabilities. Simulation tests evaluated the path tracking accuracy and overall performance of the system. Comparative results show the Pure Pursuit controller's superiority over standard position controllers in terms of accuracy, robustness, and adaptability. Additionally, this paper introduces an innovative Coverage Path Planning (CPP) strategy based on grid decomposition. Integrated with the Pure Pursuit control mechanism, this CPP strategy ensures precise path tracking and maximizes coverage uniformity, further enhancing the effectiveness and sustainability of agricultural spraying operations. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) are aerial vehicles that can go to a particular position without human control or with remote human control. It is because unmanned control mainly relies on position estimation that a lot of research has been studied on GPS spoofing attacks. Detection methods against GPS spoofing attacks mainly include monitoring RF signals or IMU sensor values inside UAVs. In this work, we analyze GPS attack and detection in an advanced autopilot system, PX4 without excluding RF detection. Recent studies have shown that GPS spoofing is unable to evade the detection using EKF sensor fusion. Therefore, this paper experiment whether the detection could be evaded by strengthening the attacker model. This paper classifies attacker models according to whether an attacker knows the true position of UAVs or the estimated position by UAV and proposed attack methods depending on each model in PX4. We inject GPS value which our attack method intends to UAV during mission flight in simulation environment. By manipulating the GPS driver code of PX4 controller, we inject GPS value the attack method wants into UAV in physical environment. Finally, we observed the innovation test ratio during GPS spoofing and demonstrate that GPS spoofing attack is possible keeping the innovation test ratio under the anomaly detection criterion. After that, we proposed our approach which scales the EKF’s Kalman gain randomly to increase the detection method’s efficiency and experiment with the attack methods. This detection method has little effect on the test ratio without the attack. But during the attack, the innovation test ratio was increased higher than the anomaly detection criterion so that the attack could be detected.

Quadrotor flying vehicles with suspended loads have a lot of applications, such as mine detection and transferring loads for delivery, or through places where ground vehicles cannot reach. In these applications, the payload will be suspended underneath the vehicle and it is subjected to large oscillations due to external disturbances or the acceleration of the vehicle itself. These oscillations add additional forces and moments to the quadrotor, and this deteriorates its performance and stability. In this paper, we developed a hardware-in-the-loop (HIL) simulation platform for a quadrotor with a suspended load system using Gazebo. The developed platform can be used to understand the dynamics of the system and design control systems to suppress the oscillation of the suspended load. An anti-swing controller that is based on time-delayed feedback of the load swing angles is implemented in the designed HIL environment to show the usefulness of the newly developed system and the effectiveness of the anti-swing controller. [ABSTRACT FROM AUTHOR]

A tail-sitter aircraft's control is a challenging task, especially during transition maneuvers where the lift and drag forces are highly non-linear. In this work, we implement a neural network (NN) capable to estimate such nonlinearities. Once they are estimated, one can propose a control scheme where these forces can correctly feed-forwarded. Our NN implementation has been programmed in C++ within the PX4 Autopilot, an open-source autopilot for drones. To ensure that this implementation does not considerably affect the autopilot's performance, the coded NN must imply light computational load. To test our approach, we have carried out a series of realistic simulations in the Software in The Loop (SITL) using the PX4 Autopilot firmware. These experiments demonstrate that the implemented NN can be used to estimate the tail-sitter aerodynamic forces to improve the control algorithms during all the flight phases of the tail-sitter aircraft: hover, cruise flight, and transition. [ABSTRACT FROM AUTHOR]

A redundant fast prototyping autopilot solution for unmanned aerial systems has been developed and successfully tested outdoors. While its low-level backbone is executed in a Raspberry PiR 3 + NAVIO2R with a backup autopilot, the computational power of an IntelR NUC mini-computer is employed to implement complex functionalities directly in SimulinkR, thus including in-flight debugging, tuning and monitoring. Altogether, the presented tool provides a flexible and user-friendly high-level environment with enhanced computational capabilities, which drastically reduces the prototyping timespans of complex algorithms -between 50% and 75%, according to our long and proven experience in aerial robotics-, while preventing incidents thanks to its redundant design with a human-in-the-loop pilot on the reliable PX4. Three typical outdoor cases are carried out for validation in real-life scenarios, all mounted in a DJI© F550 platform. Full integration results and telemetry for more than 50 hours of outdoor flight tests are provided.

This work designed a disturbance-observer-based nonlinear sliding mode surface controller (SMC) and validated the controller using a simulated PX4-conducted quadcopter. To achieve this goal, this research (1) developed a dynamic mathematical model; (2) built a PX4-based simulated UAV following the model-based design process; (3) developed appropriate sliding mode control laws for each degree of freedom; (4) implemented disturbance observers on the proposed SMC controller to achieve finer disturbance rejection such as crosswind effect and other mutational disturbances; (5) optimized the SMC controller’s parameters based on particle swarm optimization (PSO) method; and (6) evaluated and compared the quadcopter’s tracking performance under a range of noise and disturbances. Comparisons of PID control strategies against the SMC were documented under the same conditions. Consequently, the SMC controller with disturbance observer facilitates accurate and fast UAV adaptation in uncertain dynamic environments.

The micro air vehicle link (MAVLink in short) is a communication protocol for unmanned systems (e.g., drones and robots). It specifies a comprehensive set of messages exchanged between unmanned systems and ground stations. This protocol is used in major autopilot systems, mainly ArduPilot and PX4, and provides powerful features not only for monitoring and controlling unmanned systems missions but also for their integration into the Internet. However, there is no technical survey and/or tutorial in the literature that presents these features or explains how to make use of them. Most of the references are online tutorials and basic technical reports, and none of them presents comprehensive and systematic coverage of the protocol. In this paper, we address this gap, and we propose an overview of the MAVLink protocol, the difference between its versions, and it is potential in enabling Internet connectivity to unmanned systems. We also discuss the security aspects of the MAVLink. To the best of our knowledge, this is the first technical survey and tutorial on the MAVLink protocol, which represents an important reference for unmanned systems users and developers.

Quadrotor flying vehicles with suspended loads have a lot of applications, such as mine detection and transferring loads for delivery, or through places where ground vehicles cannot reach. In these applications, the payload will be suspended underneath the vehicle and it is subjected to large oscillations due to external disturbances or the acceleration of the vehicle itself. These oscillations add additional forces and moments to the quadrotor, and this deteriorates its performance and stability. In this paper, we developed a hardware-in-the-loop (HIL) simulation platform for a quadrotor with a suspended load system using Gazebo. The developed platform can be used to understand the dynamics of the system and design control systems to suppress the oscillation of the suspended load. An anti-swing controller that is based on time-delayed feedback of the load swing angles is implemented in the designed HIL environment to show the usefulness of the newly developed system and the effectiveness of the anti-swing controller.

The most common filters used to determine the angular position of quadrotors are the Kalman filter and the complementary filter. The problem of angular position estimation consist is a result of the absence of direct data. The most common sensors on board UAVs are micro electro mechanical system (MEMS) type sensors. The data acquired from the sensors are processed using digital filters. In the literature, the results of research conducted on the effectiveness of Kalman and complementary filters are known. A significant problem in evaluating the performance of the studied filters was the lack of an arbitrarily determined UAV position. The authors of this paper undertook the task of determining the best filter for a real object. The main objective of this research was to improve the stability of the physical quadrotor. For this purpose, we developed a research method using a laboratory station for testing quadrotor drones. Moreover, using the MATLAB environment, they determined the optimal parameters for the real filter applied using the PX4 software, which is new and has not been considered before in the available scientific literature. It should be mentioned that the authors of this work focused on the analysis of filters most commonly used for flight stabilization, without modifying the structure of these filters. By not modifying the filter structure, it is possible to optimize the existing flight controllers. The main contribution of this study lies in finding the most optimal filter, among those available in flight controllers, for angular position estimation. The special emphasis of our work was to develop a procedure for selecting the filter coefficients for a real object. The algorithm was designed so that other researchers could use it, provided they collected arbitrary data for their objects. Selected results of the research are presented in graphical form. The proposed procedure for improving the embedded filter can be used by other researchers on their subjects.

Unmanned aerial vehicles (UAVs) are affordable these days. For that reason, there are currently examples of the use of UAVs in recreational, professional and research applications. Most of the commercial UAVs use Px4 for their operating system. Even though Px4 allows one to change the flight controller structure, the proportional-integral-derivative (PID) format is still by far the most popular choice. A selection of the PID controller parameters is required before the UAV can be used. Although there are guidelines for the design of PID parameters, they do not guarantee the stability of the UAV, which in many cases, leads to collisions involving the UAV during the calibration process. In this paper, an offline tuning procedure based on the multi-objective particle swarm optimization (MOPSO) algorithm for the attitude and altitude control of a Px4-based UAV is proposed. A Pareto dominance concept is used for the MOPSO to find values for the PID comparing parameters of step responses (overshoot, rise time and root-mean-square). Experimental results are provided to validate the proposed tuning procedure by using a quadrotor as a case study.

Quadrotors with slung loads have many real-life applications, therefore a new fuzzy logic controller (FLC) is proposed to suppress the oscillations of this load. The output of the proposed FLC will be an additional trajectory that is added to the quadrotor original trajectory. The integration of the proposed FLC with the existing quadrotor autopilot such PX4 will be easy and it will not need any modification to the original structure of the autopilot. The rules of the proposed FLC are generated to mimic the performance of the anti-swing controller that depends on the time-delayed feedback of the load swing angles. The optimal distribution of the membership functions is determined by the genetic algorithms technique such that the load swing history is minimal. The simulation results show that the suggested geno-fuzzy controller is able to stabilize the vehicle and reduce the slung load oscillations under different flight conditions. Moreover, it can be implemented directly on the existing flight controllers without making any modification to the code of the original firmware. [ABSTRACT FROM AUTHOR]

The most common filters used to determine the angular position of quadrotors are the Kalman filter and the complementary filter. The problem of angular position estimation consist is a result of the absence of direct data. The most common sensors on board UAVs are micro electro mechanical system (MEMS) type sensors. The data acquired from the sensors are processed using digital filters. In the literature, the results of research conducted on the effectiveness of Kalman and complementary filters are known. A significant problem in evaluating the performance of the studied filters was the lack of an arbitrarily determined UAV position. The authors of this paper undertook the task of determining the best filter for a real object. The main objective of this research was to improve the stability of the physical quadrotor. For this purpose, we developed a research method using a laboratory station for testing quadrotor drones. Moreover, using the MATLAB environment, they determined the optimal parameters for the real filter applied using the PX4 software, which is new and has not been considered before in the available scientific literature. It should be mentioned that the authors of this work focused on the analysis of filters most commonly used for flight stabilization, without modifying the structure of these filters. By not modifying the filter structure, it is possible to optimize the existing flight controllers. The main contribution of this study lies in finding the most optimal filter, among those available in flight controllers, for angular position estimation. The special emphasis of our work was to develop a procedure for selecting the filter coefficients for a real object. The algorithm was designed so that other researchers could use it, provided they collected arbitrary data for their objects. Selected results of the research are presented in graphical form. The proposed procedure for improving the embedded filter can be used by other researchers on their subjects. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are affordable these days. For that reason, there are currently examples of the use of UAVs in recreational, professional and research applications. Most of the commercial UAVs use Px4 for their operating system. Even though Px4 allows one to change the flight controller structure, the proportional-integral-derivative (PID) format is still by far the most popular choice. A selection of the PID controller parameters is required before the UAV can be used. Although there are guidelines for the design of PID parameters, they do not guarantee the stability of the UAV, which in many cases, leads to collisions involving the UAV during the calibration process. In this paper, an offline tuning procedure based on the multi-objective particle swarm optimization (MOPSO) algorithm for the attitude and altitude control of a Px4-based UAV is proposed. A Pareto dominance concept is used for the MOPSO to find values for the PID comparing parameters of step responses (overshoot, rise time and root-mean-square). Experimental results are provided to validate the proposed tuning procedure by using a quadrotor as a case study. [ABSTRACT FROM AUTHOR]