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Zhou, De Xin, Xin Chao Ma, and Teng Da Ma. "Path Planning of Quadrotor Based on Quantum Particle Swarm Optimization Algorithm." Advanced Materials Research 760-762 (September 2013): 2018–22. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.2018.
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Nowadays, it becomes a hot research topic for autonomous flight of Quadrotor in the complex environment and the realization of fully autonomous flight is still a big challenge. The path planning of unmanned aerial vehicle is a key problem for its autonomous flight. For the path planning of Quadrotor, using the quantum particle swarm optimization algorithm, and made a lot of simulation and actual flight experiments. The results of simulation and actual flight experiment show that the using of QPSO for the path planning of Quadrotor is able to obtain a satisfactory result.
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Basri, M. A., and A. Noordin. "Optimal backstepping control of quadrotor UAV using gravitational search optimization algorithm." Bulletin of Electrical Engineering and Informatics 9, no. 5 (October 1, 2020): 1819–26. http://dx.doi.org/10.11591/eei.v9i5.2159.
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Quadrotor unmanned aerial vehicle (UAV) has superior characteristics such as ability to take off and land vertically, to hover in a stable air condition and to perform fast maneuvers. However, developing a high-performance quadrotor UAV controller is a difficult problem as quadrotor is an unstable and underactuated nonlinear system. The effort in this article focuses on designing and optimizing an autonomous quadrotor UAV controller. First, the aerial vehicle's dynamic model is presented. Then it is suggested an optimal backstepping controller (OBC). Traditionally, backstepping controller (BC) parameters are often selected arbitrarily. The gravitational search algorithm (GSA) is used here to determine the BC parameter optimum values. In the algorithm, the control parameters are calculated using an integral absolute error to minimize the fitness function. As the control law is based on the theorem of Lyapunov, the asymptotic stability of the scheme can be ensured. Finally, several simulation studies are conducted to show the efficacy of the suggested OBC.
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Jembre, Yalew Zelalem, Yuniarto Wimbo Nugroho, Muhammad Toaha Raza Khan, Muhammad Attique, Rajib Paul, Syed Hassan Ahmed Shah, and Beomjoon Kim. "Evaluation of Reinforcement and Deep Learning Algorithms in Controlling Unmanned Aerial Vehicles." Applied Sciences 11, no. 16 (August 6, 2021): 7240. http://dx.doi.org/10.3390/app11167240.
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Unmanned Aerial Vehicles (UAVs) are abundantly becoming a part of society, which is a trend that is expected to grow even further. The quadrotor is one of the drone technologies that is applicable in many sectors and in both military and civilian activities, with some applications requiring autonomous flight. However, stability, path planning, and control remain significant challenges in autonomous quadrotor flights. Traditional control algorithms, such as proportional-integral-derivative (PID), have deficiencies, especially in tuning. Recently, machine learning has received great attention in flying UAVs to desired positions autonomously. In this work, we configure the quadrotor to fly autonomously by using agents (the machine learning schemes being used to fly the quadrotor autonomously) to learn about the virtual physical environment. The quadrotor will fly from an initial to a desired position. When the agent brings the quadrotor closer to the desired position, it is rewarded; otherwise, it is punished. Two reinforcement learning models, Q-learning and SARSA, and a deep learning deep Q-network network are used as agents. The simulation is conducted by integrating the robot operating system (ROS) and Gazebo, which allowed for the implementation of the learning algorithms and the physical environment, respectively. The result has shown that the Deep Q-network network with Adadelta optimizer is the best setting to fly the quadrotor from the initial to desired position.
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XIAN, Bin. "Autonomous Control of a Micro Quadrotor Unmanned Aerial Vehicle Using Optical Flow." Journal of Mechanical Engineering 51, no. 9 (2015): 58. http://dx.doi.org/10.3901/jme.2015.09.058.
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Jiang, Tao, Defu Lin, and Tao Song. "Vision-based autonomous landing of a quadrotor using a gimbaled camera." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 14 (April 2, 2019): 5093–106. http://dx.doi.org/10.1177/0954410019837777.
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This paper proposes a novel vision-based autonomous landing scheme for micro aerial vehicles with perturbations by using a gimbaled camera. There are no sensors available on the moving target in the task. The relative position between the drone and moving target is obtained by the camera mounted on a two-degree-of-freedom pan-tile platform. Firstly, the detection algorithm runs in real time and outputs the pixel tracking errors. Then, an adaptive vision-based controller for the pan-tilt platform guarantees that the line of sight of the camera always tracks the target. Next, the relative position tracking error is approximated according to the gimbal's rotation angles. Disturbance observer-based control is applied for flight control to attenuate the effect of disturbance and recover the hign tracking performance, where relative velocity and lumped disturbances are estimated by extend disturbance observers. The proposed flight controller guarantees that the tracking errors are ultimately bounded with tunable ultimate bounds. The convergence property is demonstrated through Lyapunov theory. The simulations and experiments illustrate the effectiveness and the superiority performance of the proposed control system.
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M. Lazim, Izzuddin, Abdul Rashid Husain, Nurul Adilla Mohd Subha, and Mohd Ariffanan Mohd Basri. "Intelligent Observer-Based Feedback Linearization for Autonomous Quadrotor Control." International Journal of Engineering & Technology 7, no. 4.35 (November 30, 2018): 904. http://dx.doi.org/10.14419/ijet.v7i4.35.26280.
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The presence of disturbances can cause instability to the quadrotor flight and can be dangerous especially when operating near obstacles or other aerial vehicles. In this paper, a hybrid controller called state feedback with intelligent disturbance observer-based control (SF-iDOBC) is developed for trajectory tracking of quadrotor in the presence of time-varying disturbances, e.g. wind. This is achieved by integrating artificial intelligence (AI) technique with disturbance observer-based feedback linearization to achieve a better disturbance rejection capability. Here, the observer estimates the disturbances acting on the quadrotor, while AI technique using the radial basis function neural network (RBFNN) compensates the disturbance estimation error. To improve the error compensation of RBFNN, the k-means clustering method is used to find the optimal centers of the Gaussian activation function. In addition, the weights of the RBFNN are tuned online using the derived adaptation law based on the Lyapunov method, which eliminates the offline training. In the simulation experiment conducted, a total of four input nodes and five hidden neurons are used to compensate for the error. The results obtained demonstrate the effectiveness and merits of the theoretical development.
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Rice, Caleb, Yu Gu, Haiyang Chao, Trenton Larrabee, Srikanth Gururajan, Marcello Napolitano, Tanmay Mandal, and Matthew Rhudy. "Autonomous Close Formation Flight Control with Fixed Wing and Quadrotor Test Beds." International Journal of Aerospace Engineering 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/9517654.
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Autonomous formation flight is a key approach for reducing energy cost and managing traffic in future high density airspace. The use of Unmanned Aerial Vehicles (UAVs) has allowed low-budget and low-risk validation of autonomous formation flight concepts. This paper discusses the implementation and flight testing of nonlinear dynamic inversion (NLDI) controllers for close formation flight (CFF) using two distinct UAV platforms: a set of fixed wing aircraft named “Phastball” and a set of quadrotors named “NEO.” Experimental results show that autonomous CFF with approximately 5-wingspan separation is achievable with a pair of low-cost unmanned Phastball research aircraft. Simulations of the quadrotor flight also validate the design of the NLDI controller for the NEO quadrotors.
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Loianno, Giuseppe, Yash Mulgaonkar, Chris Brunner, Dheeraj Ahuja, Arvind Ramanandan, Murali Chari, Serafin Diaz, and Vijay Kumar. "Autonomous flight and cooperative control for reconstruction using aerial robots powered by smartphones." International Journal of Robotics Research 37, no. 11 (September 2018): 1341–58. http://dx.doi.org/10.1177/0278364918774136.
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Advances in consumer electronics products and the technology seen in personal computers, digital cameras, and smartphones phones have led to the price/performance ratio of sensors and processors falling dramatically over the last decade. In particular, many consumer products are packaged with small cameras, gyroscopes, and accelerometers, all sensors that are needed for autonomous robots in GPS-denied environments. The low mass and small form factor make them particularly well suited for autonomous flight with small flying robots. In this work, we present the first fully autonomous smartphone-based system for quadrotors. We show how multiple quadrotors can be stabilized and controlled to achieve autonomous flight in indoor buildings with application to smart homes, search and rescue, monitoring construction projects, and developing models for architecture design. In our work, the computation for sensing and control runs on an off-the-shelf smartphone, with all the software functionality embedded in a smartphone app. No additional sensors or processors are required for autonomous flight. We are also able to use multiple, coordinated autonomous aerial vehicles to improve the efficiency of our mission. In our framework, multiple vehicles are able to plan safe trajectories avoiding inter-robot collisions, while concurrently building in a cooperative manner a three-dimensional map of the environment. The work allows any consumer with any number of robots equipped with smartphones to autonomously drive a team of quadrotor robots, even without GPS, by downloading our app and cooperatively build three-dimensional maps.
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Lin, Lishan, Yuji Yang, Hui Cheng, and Xuechen Chen. "Autonomous Vision-Based Aerial Grasping for Rotorcraft Unmanned Aerial Vehicles." Sensors 19, no. 15 (August 3, 2019): 3410. http://dx.doi.org/10.3390/s19153410.
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Autonomous vision-based aerial grasping is an essential and challenging task for aerial manipulation missions. In this paper, we propose a vision-based aerial grasping system for a Rotorcraft Unmanned Aerial Vehicle (UAV) to grasp a target object. The UAV system is equipped with a monocular camera, a 3-DOF robotic arm with a gripper and a Jetson TK1 computer. Efficient and reliable visual detectors and control laws are crucial for autonomous aerial grasping using limited onboard sensing and computational capabilities. To detect and track the target object in real time, an efficient proposal algorithm is presented to reliably estimate the region of interest (ROI), then a correlation filter-based classifier is developed to track the detected object. Moreover, a support vector regression (SVR)-based grasping position detector is proposed to improve the grasp success rate with high computational efficiency. Using the estimated grasping position and the UAV?Äôs states, novel control laws of the UAV and the robotic arm are proposed to perform aerial grasping. Extensive simulations and outdoor flight experiments have been implemented. The experimental results illustrate that the proposed vision-based aerial grasping system can autonomously and reliably grasp the target object while working entirely onboard.
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Armendariz, Saul, Victor Becerra, and Nils Bausch. "Bio-inspired Autonomous Visual Vertical and Horizontal Control of a Quadrotor Unmanned Aerial Vehicle." Electronics 8, no. 2 (February 5, 2019): 184. http://dx.doi.org/10.3390/electronics8020184.
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Near-ground manoeuvres, such as landing, are key elements in unmanned aerial vehicle navigation. Traditionally, these manoeuvres have been done using external reference frames to measure or estimate the velocity and the height of the vehicle. Complex near-ground manoeuvres are performed by flying animals with ease. These animals perform these complex manoeuvres by exclusively using the information from their vision and vestibular system. In this paper, we use the Tau theory, a visual strategy that, is believed, is used by many animals to approach objects, as a solution for relative ground distance control for unmanned vehicles. In this paper, it is shown how this approach can be used to perform near-ground manoeuvres in a vertical and horizontal manner on a moving target without the knowledge of height and velocity of either the vehicle or the target. The proposed system is tested with simulations. Here, it is shown that, using the proposed methods, the vehicle is able to perform landing on a moving target, and also they enable the user to choose the dynamic characteristics of the approach.
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Mohammadi, Mostafa, Davide Bicego, Antonio Franchi, Davide Barcelli, and Domenico Prattichizzo. "Aerial Tele-Manipulation with Passive Tool via Parallel Position/Force Control." Applied Sciences 11, no. 19 (September 26, 2021): 8955. http://dx.doi.org/10.3390/app11198955.
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This paper addresses the problem of unilateral contact interaction by an under-actuated quadrotor UAV equipped with a passive tool in a bilateral teleoperation scheme. To solve the challenging control problem of force regulation in contact interaction while maintaining flight stability and keeping the contact, we use a parallel position/force control method, commensurate to the system dynamics and constraints in which using the compliant structure of the end-effector the rotational degrees of freedom are also utilized to attain a broader range of feasible forces. In a bilateral teleoperation framework, the proposed control method regulates the aerial manipulator position in free flight and the applied force in contact interaction. On the master side, the human operator is provided with force haptic feedback to enhance his/her situational awareness. The validity of the theory and efficacy of the solution are shown by experimental results. This control architecture, integrated with a suitable perception/localization pipeline, could be used to perform outdoor aerial teleoperation tasks in hazardous and/or remote sites of interest.
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Sadeghzadeh, Iman, Mahyar Abdolhosseini, and Youmin Zhang. "Payload Drop Application Using an Unmanned Quadrotor Helicopter Based on Gain-Scheduled PID and Model Predictive Control." Unmanned Systems 02, no. 01 (January 2014): 39–52. http://dx.doi.org/10.1142/s2301385014500034.
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Two useful control techniques are investigated and applied experimentally to an unmanned quadrotor helicopter for a practical and important scenario of using an Unmanned Aerial Vehicle (UAV) for dropping a payload in circumstances where search and rescue and delivery of supplies and goods is dangerous and difficult to reach environments such as forest or high building fires fighting, rescue in earthquake, flood and nuclear disaster situations. The two considered control techniques for such applications are the Gain-Scheduled Proportional-Integral-Derivative (GS-PID) control and the Model Predictive Control (MPC). Both the model-free (GS-PID) and model-based (MPC) algorithms show a very promising performance with application to taking-off, height holding, payload dropping, and landing periods in a payload dropping mission. Finally, both algorithms are successfully implemented on an unmanned quadrotor helicopter testbed (known as Qball-X4) available at the Networked Autonomous Vehicles Lab (NAVL) of Concordia University for payload dropping tests to illustrate the effectiveness and performance comparison of the two control techniques.
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Mohd Basri, Mohd Ariffanan, Abdul Rashid Husain, and Kumeresan A. Danapalasingam. "Nonlinear Control of an Autonomous Quadrotor Unmanned Aerial Vehicle using Backstepping Controller Optimized by Particle Swarm Optimization." Journal of Engineering Science and Technology Review 8, no. 3 (June 2015): 39–45. http://dx.doi.org/10.25103/jestr.083.05.
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Liu, Xu, Bo Chen, Yuqing He, and Decai Li. "Development of an autonomous object transfer system by an unmanned aerial vehicle based on binocular vision." International Journal of Advanced Robotic Systems 17, no. 1 (January 1, 2020): 172988142090773. http://dx.doi.org/10.1177/1729881420907732.
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This article describes the development of an unmanned aerial vehicle system that had a remarkable performance in the 6th International Unmanned Aerial Vehicle Innovation Grand Prix, which was held on November 2–4, 2018, in Anji, China. The main mission of the competition was to build a simulated tower using prefabricated components by an unmanned rotorcraft, which could be decomposed into the following four subtasks: (1) navigation and control, (2) recognition and location, (3) grasp and construction, and (4) task planning and scheduling. All the tasks were required to perform autonomously without human intervention. According to the requirement of the mission, the unmanned aerial vehicle system was designed and implemented with high degree of autonomy and reliability, whose hardware was developed on a quadrotor platform by integrating various system components, including sensors, computers, power, and grasp mechanism. Software algorithms were exploited, and executable computer codes were implemented and integrated with the developed unmanned aerial vehicle hardware system. Integration of the two provided onboard intelligence to complete the mission. This article addresses the major components and development process of the unmanned aerial vehicle system and describes its applications to the competition mission.
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Liu, Xuancen, Yueneng Yang, Chenxiang Ma, Jie Li, and Shifeng Zhang. "Real-Time Visual Tracking of Moving Targets Using a Low-Cost Unmanned Aerial Vehicle with a 3-Axis Stabilized Gimbal System." Applied Sciences 10, no. 15 (July 23, 2020): 5064. http://dx.doi.org/10.3390/app10155064.
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Unmanned Aerial Vehicles (UAVs) have recently shown great performance collecting visual data through autonomous exploration and mapping, which are widely used in reconnaissance, surveillance, and target acquisition (RSTA) applications. In this paper, we present an onboard vision-based system for low-cost UAVs to autonomously track a moving target. Real-time visual tracking is achieved by using an object detection algorithm based on the Kernelized Correlation Filter (KCF) tracker. A 3-axis gimbaled camera with separate Inertial Measurement Unit (IMU) is used to aim at the selected target during flights. The flight control algorithm for tracking tasks is implemented on a customized quadrotor equipped with an onboard computer and a microcontroller. The proposed system is experimentally validated by successfully chasing a ground and aerial target in an outdoor environment, which has proven its reliability and efficiency.
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Rodríguez-Abreo, Omar, Juan Manuel Garcia-Guendulain, Rodrigo Hernández-Alvarado, Alejandro Flores Rangel, and Carlos Fuentes-Silva. "Genetic Algorithm-Based Tuning of Backstepping Controller for a Quadrotor-Type Unmanned Aerial Vehicle." Electronics 9, no. 10 (October 21, 2020): 1735. http://dx.doi.org/10.3390/electronics9101735.
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Backstepping is a control technique based on Lyapunov’s theory that has been successfully implemented in the control of motors and robots by several nonlinear methods. However, there are no standardized methods for tuning control gains (unlike the PIDs). This paper shows the tuning gains of the backstepping controller, using Genetic Algorithms (GA), for an Unmanned Aerial Vehicle (UAV), quadrotor type, designed for autonomous trajectory tracking. First, a dynamic model of the vehicle is obtained through the Newton‒Euler methodology. Then, the control law is obtained, and self-tuning is performed, through which we can obtain suitable values of the gains in order to achieve the design requirements. In this work, the establishment time and maximum impulse are considered as such. The tuning and simulations of the system response were performed using the MATLAB-Simulink environment, obtaining as a result the compliance of the design parameters and the correct tracking of different trajectories. The results show that self-tuning by means of genetic algorithms satisfactorily adjusts for the gains of a backstepping controller applied to a quadrotor and allows for the implementation of a control system that responds appropriately to errors of different magnitude.
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Rodriguez-Castaño, Angel, Saeed Rafee Nekoo, Honorio Romero, Rafael Salmoral, José Ángel Acosta, and Anibal Ollero. "Installation of Clip-Type Bird Flight Diverters on High-Voltage Power Lines with Aerial Manipulation Robot: Prototype and Testbed Experimentation." Applied Sciences 11, no. 16 (August 12, 2021): 7427. http://dx.doi.org/10.3390/app11167427.
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This work presents the application of an aerial manipulation robot for the semi-autonomous installation of clip-type bird flight diverters on overhead power line cables. A custom-made prototype is designed, developed, and experimentally validated. The proposed solution aims to reduce the cost and risk of current procedures carried out by human operators deployed on suspended carts, lifts, or manned helicopters. The system consists of an unmanned aerial vehicle (UAV) equipped with a custom-made tool. This tool allows the high force required for the diverter installation to be generated; however, it is isolated from the aerial robot through a passive joint. Thus, the aerial robot stability is not compromised during the installation. This paper thoroughly describes the designed prototype and the control system for semi-autonomous operation. Flight experiments conducted in an illustrative scenario validate the performance of the system; the tests were carried out in an indoor testbed using a power line cable mock-up.
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Zhao, Yinghao, Li Yan, Yu Chen, Jicheng Dai, and Yuxuan Liu. "Robust and Efficient Trajectory Replanning Based on Guiding Path for Quadrotor Fast Autonomous Flight." Remote Sensing 13, no. 5 (March 4, 2021): 972. http://dx.doi.org/10.3390/rs13050972.
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Path planning is one of the key parts of unmanned aerial vehicle (UAV) fast autonomous flight in an unknown cluttered environment. However, real-time and stability remain a significant challenge in the field of path planning. To improve the robustness and efficiency of the path planning method in complex environments, this paper presents RETRBG, a robust and efficient trajectory replanning method based on the guiding path. Firstly, a safe guiding path is generated by using an improved A* and path pruning method, which is used to perceive the narrow space in its surrounding environment. Secondly, under the guidance of the path, a guided kinodynamic path searching method (GKPS) is devised to generate a safe, kinodynamically feasible and minimum-time initial path. Finally, an adaptive optimization function with two modes is proposed to improve the optimization quality in complex environments, which selects the optimization mode to optimize the smoothness and safety of the path according to the perception results of the guiding path. The experimental results demonstrate that the proposed method achieved good performance both in different obstacle densities and different resolutions. Compared with the other state-of-the-art methods, the quality and success rate of the planning result are significantly improved.
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Nguyen, V. V., and E. E. Usina. "Dynamic Models of Unmanned Aerial Vehicle Manipulator Control and Stabilization." Proceedings of the Southwest State University 24, no. 4 (February 4, 2021): 200–216. http://dx.doi.org/10.21869/2223-1560-2020-24-4-200-216.
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Purpose or research. Improving guidance accuracy of robotic capture mounted on an unmanned aerial vehicle and the stability of combined aerial manipulation system is the main objective of this study. In order to achieve this goal, a particular task of developing a manipulator control system that considers joint working space of manipulator and unmanned aerial vehicle has been solved. Methods. Kinematic model of a manipulator with three degrees of freedom is proposed in this work. This is a part of air manipulation system of quadrotor. Rotary movement of two successive links is performed by means of hinge joint. Direct and inverse kinematic tasks were solved for this manipulator. Equations for dynamic model were also obtained. Dynamic response of each link is sufficient for quick stabilization of the system with little re-adjustment. Self-tuning fuzzy proportional-integral-differentiating (PID) regulator was developed based on these data to control the manipulator. Control system for each manipulator link consists of a PID regulator and a fuzzy PID output using Mamdani method. Results. Simulation of developed manipulator control system was carried out in the absence of disturbances. The proposed control system satisfies specified requirements and ensures continuous and smooth movement of manipulator links in calculated trajectory. Conclusion. The developed three-link manipulator motion control method provides a horizontal mass center shift not more than 1.25 mm, which is an acceptable result for rapid stabilization of unmanned aerial manipulator and further practical experiments.
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Feng, Yurong, Kwaiwa Tse, Shengyang Chen, Chih-Yung Wen, and Boyang Li. "Learning-Based Autonomous UAV System for Electrical and Mechanical (E&M) Device Inspection." Sensors 21, no. 4 (February 16, 2021): 1385. http://dx.doi.org/10.3390/s21041385.
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The inspection of electrical and mechanical (E&M) devices using unmanned aerial vehicles (UAVs) has become an increasingly popular choice in the last decade due to their flexibility and mobility. UAVs have the potential to reduce human involvement in visual inspection tasks, which could increase efficiency and reduce risks. This paper presents a UAV system for autonomously performing E&M device inspection. The proposed system relies on learning-based detection for perception, multi-sensor fusion for localization, and path planning for fully autonomous inspection. The perception method utilizes semantic and spatial information generated by a 2-D object detector. The information is then fused with depth measurements for object state estimation. No prior knowledge about the location and category of the target device is needed. The system design is validated by flight experiments using a quadrotor platform. The result shows that the proposed UAV system enables the inspection mission autonomously and ensures a stable and collision-free flight.
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Upadhyay, Jatin, Abhishek Rawat, and Dipankar Deb. "Multiple Drone Navigation and Formation Using Selective Target Tracking-Based Computer Vision." Electronics 10, no. 17 (September 1, 2021): 2125. http://dx.doi.org/10.3390/electronics10172125.
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Autonomous unmanned aerial vehicles work seamlessly within the GPS signal range, but their performance deteriorates in GPS-denied regions. This paper presents a unique collaborative computer vision-based approach for target tracking as per the image’s specific location of interest. The proposed method tracks any object without considering its properties like shape, color, size, or pattern. It is required to keep the target visible and line of sight during the tracking. The method gives freedom of selection to a user to track any target from the image and form a formation around it. We calculate the parameters like distance and angle from the image center to the object for the individual drones. Among all the drones, the one with a significant GPS signal strength or nearer to the target is chosen as the master drone to calculate the relative angle and distance between an object and other drones considering approximate Geo-location. Compared to actual measurements, the results of tests done on a quadrotor UAV frame achieve 99% location accuracy in a robust environment inside the exact GPS longitude and latitude block as GPS-only navigation methods. The individual drones communicate to the ground station through a telemetry link. The master drone calculates the parameters using data collected at ground stations. Various formation flying methods help escort other drones to meet the desired objective with a single high-resolution first-person view (FPV) camera. The proposed method is tested for Airborne Object Target Tracking (AOT) aerial vehicle model and achieves higher tracking accuracy.
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Martinez-Martin, Ester, Eric Ferrer, Ilia Vasilev, and Angel P. del Pobil. "The UJI Aerial Librarian Robot: A Quadcopter for Visual Library Inventory and Book Localisation." Sensors 21, no. 4 (February 4, 2021): 1079. http://dx.doi.org/10.3390/s21041079.
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Over time, the field of robotics has provided solutions to automate routine tasks in different scenarios. In particular, libraries are awakening great interest in automated tasks since they are semi-structured environments where machines coexist with humans and several repetitive operations could be automatically performed. In addition, multirotor aerial vehicles have become very popular in many applications over the past decade, however autonomous flight in confined spaces still presents a number of challenges and the use of small drones has not been reported as an automated inventory device within libraries. This paper presents the UJI aerial librarian robot that leverages computer vision techniques to autonomously self-localize and navigate in a library for automated inventory and book localization. A control strategy to navigate along the library bookcases is presented by using visual markers for self-localization during a visual inspection of bookshelves. An image-based book recognition technique is described that combines computer vision techniques to detect the tags on the book spines, followed by an optical character recognizer (OCR) to convert the book code on the tags into text. These data can be used for library inventory. Misplaced books can be automatically detected, and a particular book can be located within the library. Our quadrotor robot was tested in a real library with promising results. The problems encountered and limitation of the system are discussed, along with its relation to similar applications, such as automated inventory in warehouses.
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Yungaicela-Naula, Noe, Luis E. Garza-Castañon, Youmin Zhang, and Luis I. Minchala-Avila. "UAV-Based Air Pollutant Source Localization Using Combined Metaheuristic and Probabilistic Methods." Applied Sciences 9, no. 18 (September 6, 2019): 3712. http://dx.doi.org/10.3390/app9183712.
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Air pollution is one of the greatest risks for the health of people. In recent years, platforms based on Unmanned Aerial Vehicles (UAVs) for the monitoring of pollution in the air have been studied to deal with this problem, due to several advantages, such as low-costs, security, multitask and ease of deployment. However, due to the limitations in the flying time of the UAVs, these platforms could perform monitoring tasks poorly if the mission is not executed with an adequate strategy and algorithm. Their application can be improved if the UAVs have the ability to perform autonomous monitoring of the areas with a high concentration of the pollutant, or even to locate the pollutant source. This work proposes an algorithm to locate an air pollutant’s source by using a UAV. The algorithm has two components: (i) a metaheuristic technique is used to trace the increasing gradient of the pollutant concentration, and (ii) a probabilistic component complements the method by concentrating the search in the most promising areas in the targeted environment. The metaheuristic technique has been selected from a simulation-based comparative analysis between some classical techniques. The probabilistic component uses the Bayesian methodology to build and update a probability map of the pollutant source location, with each new sensor information available, while the UAV navigates in the environment. The proposed solution was tested experimentally with a real quadrotor navigating in a virtual polluted environment. The results show the effectiveness and robustness of the algorithm.
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Yang, Shuting, Robert Talbot, Michael Frish, Levi Golston, Nicholas Aubut, Mark Zondlo, Christopher Gretencord, and James McSpiritt. "Natural Gas Fugitive Leak Detection Using an Unmanned Aerial Vehicle: Measurement System Description and Mass Balance Approach." Atmosphere 9, no. 10 (October 1, 2018): 383. http://dx.doi.org/10.3390/atmos9100383.
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Natural gas is an abundant resource across the United States, of which methane (CH4) is the main component. About 2% of extracted CH4 is lost through leaks. The Remote Methane Leak Detector (RMLD)-Unmanned Aerial Vehicle (UAV) system was developed to investigate natural gas fugitive leaks in this study. The system is composed of three major technologies: miniaturized RMLD (mini-RMLD) based on Backscatter Tunable Diode Laser Absorption Spectroscopy (TDLAS), an autonomous quadrotor UAV and simplified quantification and localization algorithms. With a miniaturized, downward-facing RMLD on a small UAV, the system measures the column-integrated CH4 mixing ratio and can semi-autonomously monitor CH4 leakage from sites associated with natural gas production, providing an advanced capability in detecting leaks at hard-to-access sites compared to traditional manual methods. Automated leak characterization algorithms combined with a wireless data link implement real-time leak quantification and reporting. This study placed particular emphasis on the RMLD-UAV system description and the quantification algorithm development based on a mass balance approach. Early data were gathered to test the prototype system and to evaluate the algorithm performance. The quantification algorithm derived in this study tended to underestimate the gas leak rates and yielded unreliable estimations in detecting leaks under 7 × 10 − 6 m3/s (~1 Standard Cubic Feet per Hour (SCFH)). Zero-leak cases can be ascertained via a skewness indicator, which is unique and promising. The influence of the systematic error was investigated by introducing simulated noises, of which Global Positioning System (GPS) noise presented the greatest impact on leak rate errors. The correlation between estimated leak rates and wind conditions were investigated, and steady winds with higher wind speeds were preferred to get better leak rate estimations, which was accurate to approximately 50% during several field trials. High precision coordinate information from the GPS, accurate wind measurements and preferred wind conditions, appropriate flight strategy and the relative steady survey height of the system are the crucial factors to optimize the leak rate estimations.
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Araar, Oualid, Nabil Aouf, and Jose Luis Vallejo Dietz. "Power pylon detection and monocular depth estimation from inspection UAVs." Industrial Robot: An International Journal 42, no. 3 (May 18, 2015): 200–213. http://dx.doi.org/10.1108/ir-11-2014-0419.
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Purpose This paper aims to present a new vision-based approach for both the identification and the estimation of the relative distance between the unmanned aerial vehicle (UAV) and power pylon. Autonomous power line inspection using small UAVs, has been the focus of many research works over the past couple of decades. Automatic detection of power pylons is a primary requirement to achieve such autonomous systems. It is still a challenging task due to the complex geometry and cluttered background of these structures. Design/methodology/approach The identification solution proposed, avoids the complexity of classic object recognition techniques. Instead of searching the whole image for the pylon template, low-level geometric priors with robust colour attributes are combined to remove the pylon background. The depth estimation, on the other hand, is based on a new concept which exploits the ego-motion of the inspection UAV to estimate its distance from the pylon using just a monocular camera. Findings An algorithm is tested on a quadrotor UAV, using different kinds of metallic power pylons. Both simulation and real-world experiments, conducted in different backgrounds and illumination conditions, show very promising results. Research limitations/implications In the real tests carried out, the Inertial Navigation System (INS) of the vehicle was used to estimate its ego-motion. A more reliable solution should be considered for longer distances, by either fusing INS and global positioning system data or using visual navigation techniques such as visual odometry. Originality/value A simple yet efficient solution is proposed that allows the UAV to reliably identify the pylon, with still a low processing cost. Considering a monocular solution is a major advantage, given the limited payload and processing power of such small vehicles.
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Pu, Huangzhong, Ziyang Zhen, and Daobo Wang. "Modified shuffled frog leaping algorithm for optimization of UAV flight controller." International Journal of Intelligent Computing and Cybernetics 4, no. 1 (March 29, 2011): 25–39. http://dx.doi.org/10.1108/17563781111115778.
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PurposeAttitude control of unmanned aerial vehicle (UAV) is the purposeful manipulation of controllable external forces to establish a desired attitude, which is inner‐loop of the autonomous flight control system. In the practical applications, classical control methods such as proportional‐integral‐derivative control are usually selected because of simple and high reliability. However, it is usually difficult to select or optimize the control parameters. The purpose of this paper is to investigate an intelligent algorithm based classical controller of UAV.Design/methodology/approachAmong the many intelligent algorithms, shuffled frog leaping algorithm (SFLA) combines the benefits of the genetic‐based memetic algorithm as well as social behavior based particle swarm optimization. SFLA is a population based meta‐heuristic intelligent optimization method inspired by natural memetics. In order to improve the performance of SFLA, a different dividing method of the memeplexes is presented to make their performance balance; moreover, an evolution mechanism of the best frog is introduced to make the algorithm jump out the local optimum. The modified SFLA is applied to the tuning of the proportional coefficients of pitching and rolling channels of UAV flight control system.FindingsSimulation of a UAV control system in which the nonlinear model is obtained by the wind tunnel experiment show the rapid dynamic response and high control precision by using the modified SFLA optimized attitude controller, which is better than that of the original SFLA and particle swarm optimization method.Originality/valueA modification scheme is presented to improve the global searching capability of SFLA. The modified SFLA based intelligent determination method of the UAV flight controller parameters is proposed, in order to improve the attitude control performance of UAV.
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Ghasemi, Ali, Farhad Parivash, and Serajeddin Ebrahimian. "Autonomous landing of a quadrotor on a moving platform using vision-based FOFPID control." Robotica, September 20, 2021, 1–19. http://dx.doi.org/10.1017/s0263574721001181.
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Abstract This research deals with the autonomous landing maneuver of a quadrotor unmanned aerial vehicle (UAV) on an unmanned ground vehicle (UGV). It is assumed that the UGV moves independently, and there is no communication and collaboration between the two vehicles. This paper aims at the design of a closed-loop vision-based control system for quadrotor UAV to perform autonomous landing maneuvers in the possible minimum time despite the wind-induced disturbance force. In this way, a fractional-order fuzzy proportional-integral-derivative controller is introduced for the nonlinear under-actuated system of a quadrotor. Also, a feedback linearization term is included in the control law to compensate model nonlinearities. A supervisory control algorithm is proposed as an autonomous landing path generator to perform fast, smooth, and accurate landings. On the other hand, a compound AprilTag fiducial marker is employed as the target of a vision positioning system, enabling high precision relative positioning in the range between 10 and 350 cm height. A software-in-the-loop simulation testbed is realized on the windows platform. Numerical simulations with the proposed control system are carried out, while the quadrotor system is exposed to different disturbance conditions and actuator dynamics with saturated thrust output are considered.
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Glida, Hossam E., Latifa Abdou, Abdelghani Chelihi, Chouki Sentouh, and Gabriele Perozzi. "Optimal model-free fuzzy logic control for autonomous unmanned aerial vehicle." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, June 15, 2021, 095441002110253. http://dx.doi.org/10.1177/09544100211025379.
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This article deals with the issue of designing a flight tracking controller for an unmanned aerial vehicle type of quadrotor based on an optimal model-free fuzzy logic control approach. The main design objective is to perform an automatic flight trajectory tracking under multiple model uncertainties related to the knowledge of the nonlinear dynamics of the system. The optimal control is also addressed taking into consideration unknown external disturbances. To achieve this goal, we propose a new optimal model-free fuzzy logic–based decentralized control strategy where the influence of the interconnection term between the subsystems is minimized. A model-free controller is firstly designed to achieve the convergence of the tracking error. For this purpose, an adaptive estimator is proposed to ensure the approximation of the nonlinear dynamic functions of the quadrotor. The fuzzy logic compensator is then introduced to deal with the estimation error. Moreover, the optimization problem to select the optimal design parameters of the proposed controller is solved using the bat algorithm. Finally, a numerical validation based on the Parrot drone platform is conducted to demonstrate the effectiveness of the proposed control method with various flying scenarios.
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Elmokadem, Taha, and Andrey V. Savkin. "A method for autonomous collision-free navigation of a quadrotor UAV in unknown tunnel-like environments." Robotica, June 24, 2021, 1–27. http://dx.doi.org/10.1017/s0263574721000849.
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Abstract Unmanned aerial vehicles (UAVs) have become essential tools for exploring, mapping and inspection of unknown three-dimensional (3D) tunnel-like environments which is a very challenging problem. A computationally light navigation algorithm is developed in this paper for quadrotor UAVs to autonomously guide the vehicle through such environments. It uses sensors observations to safely guide the UAV along the tunnel axis while avoiding collisions with its walls. The approach is evaluated using several computer simulations with realistic sensing models and practical implementation with a quadrotor UAV. The proposed method is also applicable to other UAV types and autonomous underwater vehicles.