Academic literature on the topic 'Quadcopter Manipulator System'

In this paper, modeling, control and state estimation of a manipulating unmanned aerial vehicle (UAV) consisting of a quadcopter equipped with a two degree-of-freedom robotic manipulator is discussed. In the first step, Euler–Lagrange approach is adopted to model the coupled dynamics of the quadcopter and its robotic manipulator. Having linearized the obtained model, a linear quadratic regulator is designed to achieve simultaneous control of the quadcopter and the manipulator. Finally, a UKF-based algorithm is employed to obtain state estimation of the system. For a case study, simulation results are presented to verify feasibility of the proposed approach.

A quadcopter manipulator system is an aerial robot consisting of a quadcopter with a robotic arm attached to it. The system has coupled nonlinear dynamics with uncertain time-varying parameters. The work in this paper focuses on designing an adaptive nonlinear controller to facilitate the uncertain system’s trajectory tracking and stability. The novelty of the proposed work is the design and implementation of an adaptive feedback linearization controller, called adaptive augmented torque (AAT) control, for the aerial robot. The control law is based on a feedback linearization controller with model reference adaptive controller and a tracking error-based augmented term. Using the input-to-state stability concept, a bound on the parameter estimation error is also developed. In the presented methodology, the controller uses estimated values of system parameters obtained from the adaptive mechanism and the tracking error to compute the control input using the AAT control law. An adaptive law for estimating unknown parameters is obtained using the strictly positive real-Lyapunov method. The asymptotic stability of the closed-loop system is analyzed via the Lyapunov theory. Simulations implemented in MATLAB and ROS/Gazebo and preliminary hardware experiments are presented to validate the theoretical results and to corroborate the performance of the AAT control law.

This paper introduces the design and development of QMRS (Quadcopter Mobile Robotic System). QMRS is a real-time obstacle avoidance capability in Belarus-132N mobile robot with the cooperation of quadcopter Phantom-4. The function of QMRS consists of GPS used by Mobile Robot and image vision and image processing system from both robot and quad-copter and by using effective searching algorithm embedded inside the robot. Having the capacity to navigate accurately is one of the major abilities of a mobile robot to effectively execute a variety of jobs including manipulation, docking, and transportation. To achieve the desired navigation accuracy, mobile robots are typically equipped with on-board sensors to observe persistent features in the environment, to estimate their pose from these observations, and to adjust their motion accordingly. Quadcopter takes off from Mobile Robot, surveys the terrain and transmits the processed Image terrestrial robot. The main objective of research paper is to focus on the full coordination between robot and quadcopter by designing an efficient wireless communication using WIFI. In addition, it identify the method involving the use of vision and image processing system from both robot and quadcopter; analyzing path in real-time and avoiding obstacles based-on the computational algorithm embedded inside the robot. QMRS increases the efficiency and reliability of the whole system especially in robot navigation, image processing and obstacle avoidance due to the help and connection among the different parts of the system.

The navigation of small unmanned aerial vehicles (UAVs), such as quadcopters, significantly relies on the global positioning system (GPS); however, UAVs are vulnerable to GPS spoofing attacks. GPS spoofing is an attempt to manipulate a GPS receiver by broadcasting manipulated signals. A commercial GPS simulator can cause a GPS-guided drone to deviate from its intended course by transmitting counterfeit GPS signals. Therefore, an anti-spoofing technique is essential to ensure the operational safety of UAVs. Various methods have been introduced to detect GPS spoofing; however, most methods require additional hardware. This may not be appropriate for small UAVs with limited capacity. This study proposes a deep learning-based anti-spoofing method equipped with 1D convolutional neural network. The proposed method is lightweight and power-efficient, enabling real-time detection on mobile platforms. Furthermore, the performance of our approach can be enhanced by increasing training data and adjusting the network architecture. We evaluated our algorithm on the embedded board of a drone in terms of power consumption and inference time. Compared to the support vector machine, the proposed method showed better performance in terms of precision, recall, and F-1 score. Flight test demonstrated our algorithm could successfully detect GPS spoofing attacks.

The past decades witnessed an unprecedented urbanization and the proliferation of modern information and communication technologies (ICT), which makes the concept of Smart City feasible. Among various intelligent components, smart urban transportation monitoring is an essential part of smoothly operational smart cities. Although there is fast development of Smart Cities and the growth of Internet of Things (IoT), real-time anomalous behavior detection in Intelligent Transportation Systems (ITS) is still challenging. Because of multiple advanced features including flexibility, safety, and ease of manipulation, quadcopter drones have been widely adopted in many areas, from service improvement to urban surveillance, and data collection for scientific research. In this paper, a Smart Urban traffic Monitoring (SurMon) scheme is proposed employing drones following an edge computing paradigm. A dynamic video stream processing scheme is proposed to meet the requirements of real-time information processing and decision-making at the edge. Specifically, we propose to identify anomalous vehicle behaviors in real time by creatively applying the multidimensional Singular Spectrum Analysis (mSSA) technique in space to detect the different vehicle behaviors on roads. Multiple features of vehicle behaviors are fed into channels of the mSSA procedure. Instead of trying to create and define a database of normal activity patterns of vehicles on the road, the anomaly detection is reformatted as an outlier identifying problem. Then, a cascaded Capsules Network is designed to predict whether the behavior is a violation. An extensive experimental study has been conducted and the results have validated the feasibility and effectiveness of the SurMon scheme.

In recent years, the control of devices “by the power of the mind” has become a very controversial topic but has also been very well researched in the field of state-of-the-art gadgets, such as smartphones, laptops, tablets and even smart TVs, and also in medicine, to be used by people with disabilities for whom these technologies may be the only way to communicate with the outside world. It is well known that BCI control is a skill and can be improved through practice and training. This paper aims to improve and diversify signal processing methods for the implementation of a brain-computer interface (BCI) based on neurological phenomena recorded during motor tasks using motor imagery (MI). The aim of the research is to extract, select and classify the characteristics of electroencephalogram (EEG) signals, which are based on sensorimotor rhythms, for the implementation of BCI systems. This article investigates systems based on brain-computer interfaces, especially those that use the electroencephalogram as a method of acquisition of MI tasks. The purpose of this article is to allow users to manipulate quadcopter virtual structures (external, robotic objects) simply through brain activity, correlated with certain mental tasks using undecimal transformation (UWT) to reduce noise, Independent Component Analysis (ICA) together with determination coefficient (r2) and, for classification, a hybrid neural network consisting of Radial Basis Functions (RBF) and a multilayer perceptron–recurrent network (MLP–RNN), obtaining a classification accuracy of 95.5%. Following the tests performed, it can be stated that the use of biopotentials in human–computer interfaces is a viable method for applications in the field of BCI. The results presented show that BCI training can produce a rapid change in behavioral performance and cognitive properties. If more than one training session is used, the results may be beneficial for increasing poor cognitive performance. To achieve this goal, three steps were taken: understanding the functioning of BCI systems and the neurological phenomena involved; acquiring EEG signals based on sensorimotor rhythms recorded during MI tasks; applying and optimizing extraction methods, selecting and classifying characteristics using neuronal networks.

The article analyzes explosive objects of various types and provides their characteristics. Their unmasking feature is the material of the main part (casing), it is noted that high-explosive mines are characterized by the use of a plastic casing, for fragmentation mines – a metal casing, and the most difficult to detect are mines with a plastic casing. Methods of detecting explosive objects compared to those currently used in Ukraine are considered. Unfortunately, given the scale of mined areas, they are ineffective, so it is obviously necessary to develop more effective solutions for their detection and neutralization based on modern achievements of technical progress. Foreign countries have developed and use modern mobile robotic complexes for demining based on unmanned aerial vehicles with various types of sensors installed on them. It is expedient to create a universal detection system that can be deployed on any helicopter-type unmanned aerial vehicle (quadcopter, multicopter), on which several detection sensors are installed at the same time for the purpose of reconnaissance of the mine situation, detection of mines and their remote destruction. The article proposes the synthesis of a system for detecting explosive objects based on an unmanned aerial vehicle with a thermal imager installed on it, in combination with a metal detector and a manipulator with explosives for remote demining. The parameters of detection of explosive objects, the type of sensor sensors and the main functions of the system are considered. The algorithm of actions of the operator of the system for detecting explosive objects is proposed.

A quadcopter manipulator system (QMS) is an aerial robot comprising a quadcopter with a three degree of freedom manipulator mounted at the bottom of the vehicle. An aerial robot can reach otherwise inaccessible locations, and the manipulator can execute a variety of tasks. The presented thesis focuses on the design, control, and hardware implementation of a quadcopter manipulator system, with a robust adaptive non-linear controller. In the initial phase of the work, a thorough analysis of the workspace of the manipulator, which is mounted at the bottom of the vehicle, is presented. The proposed robotic arm has an extended workspace, allowing the end-effector to reach targets both above and below the airframe. During tasks involving interaction with walls and other structures, the drone's thrust can interact with the surroundings and cause counter moments on the system. A technique based on drone and target positions is proposed to solve this problem. A novel robust adaptive non-linear controller is designed and implemented in the second phase. With uncertain time-varying parameters, the system has coupled non-linear dynamics. A novel Augmented Adaptive Torque (AAT) control law is presented for the uncertain system, which combines a model reference adaptive controller with a feedback linearization controller. A strictly positive real-Lyapunov approach is used to create an adaptive law for estimating unknown system parameters. Lyapunov theory is used to investigate the closed-loop system's asymptotic stability. A bound on the parameter estimation error is derived utilizing the inputto- state (ISS) stability concept. The AAT control law is further combined with an estimate of the unknown bounded disturbance to create the Robust Augmented Adaptive Torque (RAAT) control law, ensuring robustness. The adaptive law is modified using a projection operator to ensure that the estimates are bounded. To validate the theoretical conclusions and corroborate the performance of the augmented adaptive torque control rule on the closed-loop system, simulations in MATLAB and ROS/Gazebo are provided. A three DoF 3D printed robotic arm is attached to an in-built quadcopter to create the QMS aerial robot, custom-built in the lab. To assess the performance of the proposed controller, real-time experiments using QMS hardware are carried out. The proposed method's efficiency is demonstrated by the aerial robot's trajectory tracking and stability during real-time testing in field experiments.

Abstract Tethered drone systems can be used to perform long-endurance tasks such as area surveillance and relay stations for wireless communication. However, all the existing systems use tethers only for data and power transmission from a stationary point on the ground. This work presents a control strategy that enables a quadcopter to follow a moving tether anchor. A force feedback controller is implemented using Fuzzy Logic. Using force-based strategy provides effective compliance between the tether’s anchor and the drone. The drone can thus be controlled by mere physical movement/manipulation of tether. This enhances the safety of current tethered drone systems and simplifies the flying of drones. Fuzzy Logic provides an intuitive edge to the control of such systems and allows handling noise in force sensors. Extensive simulation results are presented in this paper showing the effectiveness of the proposed control scheme.