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The purpose of this paper is to introduce a low-cost quadrotor that can be used for educational purposes and investigate the applicability of a low-cost MEMS laser sensor for accurate altitude control.

A single printed circuit board is designed to form the structure of the quadrotor. A low-cost MEMS motion sensor, a microcontroller and four small motors are mounted on the board. A separate laser sensor module measures the altitude. A remote controller is designed to control the quadrotor’s motion. The remote controller communicates with the quadrotor via wireless connection. Roll and pitch attitude stabilization is achieved using the proportional and derivative control algorithm. The applicability of an MEMS laser sensor for altitude control is also studied.

The low-cost quadrotor works well even though its body structure is made using a printed circuit board. Low pass and Kalman filters work well for attitude estimation and control application. The laser sensor is very accurate and good for altitude feedback; however, it has a relatively short measurement range and its sampling rate is relatively slow, which limits its applications. The vertical velocity obtained by differentiating the laser altitude has delay and inhibits suitable damping. Using the vertical velocity obtained by integrating the vertical accelerometer’s output, the damping performance is improved.

Developing a low-cost quadrotor that can be used for educational purposes and successfully implementing altitude control using a laser sensor and accelerometer.

The purpose of this paper is to present an improved particle filter-based attitude estimator for a quadrotor unmanned aerial vehicle (UAV) that addresses the degeneracy issues.

Control of a quadrotor is not sufficient enough without an estimator to eliminate the noise from low-cost sensors. In this work, particle filter-based attitude estimator is proposed and used for nonlinear quadrotor dynamics. But, since recursive Bayesian estimation steps may rise degeneracy issues, the proposed scheme is improved with four different and widely used resampling algorithms.

Robustness of the proposed schemes is tested under various scenarios that include different levels of uncertainty and different particle sizes. Statistical analyses are conducted to assess the error performance of the schemes. According to the statistical analysis, the proposed estimators are capable of reducing sensor noise up to 5x, increasing signal to noise ratio up to 2.5x and reducing the uncertainty bounds up to 36x with root mean square value of as low as 0.0024, mean absolute error value of 0.036, respectively.

To the best of the authors’ knowledge, the originality of this paper is to propose a robust particle filter-based attitude estimator to eliminate the low-cost sensor errors of quadrotor UAVs.

The purpose of this paper is to propose an improved optimization method for image matching problem, which is based on multi-scale Gaussian mutation pigeon-inspired optimization (MGMPIO) algorithm, with the objective of accomplishing the complicated image matching quickly.

The hybrid model of multi-scale Gaussian mutation (MGM) mechanism and pigeon-inspired optimization (PIO) algorithm is established for image matching problem. The MGM mechanism is a nonlinear model, which can adjust the position of pigeons by mutation operation. In addition, the variable parameter (VP) mechanism is exploited to adjust the map and compass factor of the original PIO. Low-cost quadrotor, a type of electric multiple rotorcraft, is used as a carrier of binocular camera to obtain the images.

This work improved the PIO algorithm by modifying the search strategy and adding some limits, so that it can have better performance when applied to the image matching problem. Experimental results show that the proposed method demonstrates satisfying performance in convergence speed, robustness and stability.

The proposed MGMPIO algorithm can be easily applied to solve practical problems and accelerate convergence speed of the original PIO, and thus enhancing the speed of matching process, which will considerably increase the effectiveness of algorithm.

A hybrid model of the MGM mechanism and PIO algorithm is proposed for image matching problem. The VP mechanism and low-cost quadrotor is also utilized in image matching problem.

The control of a quadrotor unmanned aerial vehicle (UAV) is a challenging problem because of its highly nonlinear dynamics, under-actuated nature and strong cross-couplings. To solve this problem, this paper aims to propose a robust control strategy, based on a concept of active disturbance rejection control (ADRC).

The altitude/attitude dynamics of a quadrotor is reformulated into the ADRC framework. Three distinct variations of the error-based ADRC algorithms, with different structures of generalized extended state observers (GESO), are derived for the altitude/attitude trajectory-following task. The convergence of the observation part is proved based on the singular perturbation theory. Through a frequency analysis and a quantitative comparison in a simulated environment, each design is shown to have certain advantages and disadvantages in terms of tracking accuracy and robustness. The digital prototypes of the proposed controllers for quadrotor altitude and attitude control channels are designed and validated through real-time hardware-in-the-loop (HIL) co-simulation, with field-programmable gate array (FPGA) hardware.

The effects of unavailable reference time-derivatives can be estimated by the ESO and rejected through the outer control loop. The higher order ESOs demonstrate better performances, but with reductions of stability margins. Time-domain simulation analysis reveals the benefits of the proposed control structure related to classical control approach. Real-time FPGA-based HIL co-simulations validated the performances of the considered digital controllers in typical quadrotor flight scenarios.

The conducted study forms a set of practical guidelines for end-users for selecting specific ADRC design for quadrotor control depending on the given control objective and work conditions. Furthermore, the paper presents detailed procedure for the design, simulation and validation of the embedded FPGA-based quadrotor control unit.

In light of the currently available literature on error-based ADRC, a comprehensive approach is applied here, which includes the design of error-based ADRC with different GESOs, its frequency-domain and time-domain analyses using different simulation of UAV flight scenarios, as well as its FPGA-based implementation and testing on the real hardware.

The purpose of this paper is to present a control strategy which uses two independent PID controllers to realize the hovering control for unmanned aerial systems (UASs). In addition, the aim of using two PID controller is to achieve the position control and velocity control simultaneously.

The dynamic of the UASs is mathematically modeled. One PID controller is used for position tracking control, while the other is selected for the vertical component of velocity tracking control. Meanwhile, fuzzy logic algorithm is presented to use the actual horizontal component of velocity to compute the desired position.

Based on this fuzzy logic algorithm, the control error of the horizontal component of velocity tracking control is narrowed gradually to be zero. The results show that the fuzzy logic algorithm can make the UASs hover still in the air and vertical to the ground.

The acquired results are based on simulation not experiment.

This is the first study to use two independent PID controllers to realize stable hovering control for UAS. It is also the first to use the velocity of the UAS to calculate the desired position.

A vision-assisted fuzzy adaptive sliding mode controller is presented in this research and implemented on a nonlinear helicopter model, which is about to land on a moving ship. Stabilization of the dynamics and tracking the landing path are required, at the same time. This study aims to take one step closer to fully autonomous landing, which is a growing trend.

An integrated guidance and control is considered for the model helicopter. A fuzzy logic is designed to adaptively choose the best control parameters for the sliding mode controller and solve the challenge of parameter tuning. A self-organizing matrix consisting of fuzzy sliding mode parameters is formed instead of a single parameter with the goal of enhancing controller tracking capability. A simple, precise and fast image recognition system based on OpenCV is used to detect the proper point for descending without getting any special data from the ship and by only using a general “H” sign.

The problem is simulated under intense disturbances, while the approach and landing performances are acceptable. Controller performance is compared and validated. Simulation results show the robustness, agility, stability and outperformance of the proposed controller.

The novelty of this paper is the designed procedure for using a simple image recognition system in the process of autonomous ship-landing, which does not use any special data sent from the ship. Besides, an improved nonlinear controller is designed for integrated guidance and control in this specific application.

In the previous decade, unmanned aerial vehicles (UAVs) have turned into a subject of enthusiasm for some exploration associations. UAVs are discovering applications in different regions going from military applications to activity reconnaissance. The purpose of this paper is to overview a particular sort of UAV called quadrotor or quadcopter.

This paper includes the dynamic models of a quadrotor and the distinctive model-reliant and model-autonomous control systems and their correlation.

In the present time, focus has moved to outlining autonomous quadrotors. Ultimately, the paper examines the potential applications of quadrotors and their part in multi-operators frameworks.

This investigation deals with the review on various quadrotors, their applications and motion control strategies.

The purpose of this paper was to present the process of building hardware and software for a collision avoidance system of a quadrotor capable of an indoor autonomous flight.

The system development was carried out in two steps. First, the quadrotor system was designed to mount mission equipments for an indoor flight. The prediction error minimization (PEM) method was used for system identification of the quadrotor, and the linear quadratic regulator (LQR) control method was used for the attitude control. Second, a collision detection system was realized by using a Kinect sensor, an embedded board and a ground control system (GCS). A Kinect sensor with embedded board can send the 3D depth information to GCS and then the GCS displays the 3D depth information with a warning message.

As the controller design requires a linear model, the PEM method was used in system identification. The LQR was used in controller design. It was found that the use of the PEM method for system identification was effective for developing a linear model required for a practical control system using LQR. As 3D depth information from a Kinect sensor is quite accurate in an indoor environment, a collision detection system with Kinect was successfully developed.

The step-by-step approach presented in this paper can be used to develop an autonomous aerial vehicle capable of navigating in an indoor environment with obstacles.

The primary contribution of the paper is the presentation of a practical method for developing a low-cost collision avoidance system for a quadrotor vehicle.

The purpose of this paper is to introduce an artificial bee colony-based Kalman filter algorithm along with an extended objective function to ensure the optimality of the estimator of the quadrotor in the presence of unknown measurement noise statistics.

Six degree-of-freedom mathematical model of the quadrotor is derived. Position controller for the quadrotor is designed. Kalman filter-based estimation algorithm is implemented in the sensor feedback loop. Artificial bee colony-based hybrid algorithm is used as an optimization method to handle the unknown noise statistics. Existing objective function is extended with a penalty term. Mathematical proof of the extended objective function is derived. Results of the proposed algorithm is compared with de facto genetic algorithm-based Kalman filter.

Artificial bee colony algorithm-based Kalman filter and extended objective function duo are able to optimize the measurement noise covariance matrix with an absolute error as low as 0.001 [m²]. Proposed method and function is capable of reducing the noise from 2 to 0.09 [m] for x-axis, 3.4 to 0.14 [m] for y-axis and 3.7 to 0.2 [m] for z-axis, respectively.

The motivation behind this paper is to bring a novel optimization-based solution for the estimation problem of the quadrotor when the measurement noise statistics are unknown along with an extended objective function to prevent the infeasible solutions with mathematical convergence analysis.

Although windy weather conditions have a significant effect on the flight safety and stability of any aircraft, the fact that quadrotors are lighter than other aircraft makes them more sensitive to the wind. This study aims to examine the extent to which quadrotors and their sensors, which are used in many fields and whose use is expected to increase significantly in the future, are affected by wind.

Flight experiments were carried out on different routes assigned by using Pixhawk Holybro 4 and Radiolink flight controllers. In these flight experiments, quadrotors were exposed to winds at different speeds and directions.

In the flight experiments, the deviation amounts in the quadrotor’s route at which wind speed was determined, and it was seen that these deviations were very serious and affected the safe flight at high wind speeds. According to the sensor information obtained from both different quadrotors’ flight experiments at different wind speeds, it was determined that the wind decreased the sensors’ accuracy.

It is foreseen that the data obtained in this study will be a source to be used in the design of quadrotors to be used in public areas in the future and to take the wind into account for safe flight.

In this study, numerous flight tests were carried out experimentally at various speeds from low speeds to high speeds on different routes using different flight controllers. The deviation data on the obtained routes and the effect of the wind on the sensors are experienced in real atmospheric conditions.