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An integrated GPS/MEMS-IMU navigation system for an autonomous helicopter
Abstract
During the last years, there is an increasing demand for cheap and easy to operate platforms for surveillance and reconnaissance purposes. Therefore, the development of micro aerial vehicles is receiving an increasing attention. However, VTOL-MAVs often show an inherent instability that makes at least an automatic stabilization necessary, because otherwise the operator would not be able to keep these vehicles airborne. This requires the availability of navigation information, especially the vehicle's attitude has to be known. This paper addresses the development of an integrated navigation system based on MEMS inertial sensors and GPS for a VTOL-MAV. Special attention is paid to the handling of GPS outages. While usually periods without GPS aiding can be bridged using the unaided strapdown solution, the poor quality of the MEMS inertial sensors prohibits this approach here. Therefore, during GPS outages the accelerometer data is interpreted as approximate measurements of the local gravity vector. Additionally, the usage of a magnetometer providing measurements of the Earth's magnetic field is motivated and discussed. Finally, flight test results illustrate the performance of the resulting system, proving that the achieved attitude accuracy is sufficient for the automatic control of the MAV. This holds in situations with permanent GPS loss and dynamic maneuvering, too.
... In [50], both a loosely coupled and a tightly coupled system for a helicopter-type UAV under various conditions imposed on the flight path, as well as under various conditions of availability of GNSS signals, is considered. The problem of development of an integrated system for helicopter-type UAVs in the presence of a magnetic sensor in addition to the integrated INS/SNS system is considered in [51]. In [52], the problem of creating a tightly coupled system for an UAV copter based on commercially available INS and GNSS navigation sensors with multiple antennas is considered. ...
... On this basis, the researchers came up with the idea of transforming integration techniques, with a slight increase in the complexity and cost of the final product, suggesting to rely more on the properties of INS sensors. It became possible by introducing additional sensors into the integrated system, namely magnetic sensors [51], thereby forming an attitude heading reference system (AHRS) and separating the processes of estimating the attitude and position of the object. In [65], it is proposed to use a nonlinear complementary filter to estimate the gyro sensor bias and the attitude of the object. ...
... The listed results of the work are more applicable to ground equipment. Some researchers offer interesting solutions with magnetometers for air vehicles, for example in [50] or [51]. ...
Navigation systems are of interest for applications in both civilian and military vehicles. Satellite navigation systems and inertial navigation systems are the most applied in this area. They have complementary properties, which has led to a trend of integrating these systems. At present, there are several approaches to GNSS/INS integration: loosely coupled, tightly coupled and deeply coupled and many approaches to their modifications in dependence of application and arising problems with measurements, such as lack of GNSS measurements or poor quality of GNSS and INS measurements. This article presents an extensive review of the available modern approaches and their modifications for integrating INS and GNSS measurements, arranging them and highlights the main problems arising for the considered type of integration approach. The article includes a review of various integration tools based on the Kalman filter and intelligent systems, INS mechanization and features of development of an INS measurement error model that is necessary for integration, the main problems of GNSS/INS integration and a comparative description of the solutions proposed by the authors for solving these problems. The findings of this work are useful for further research in the field of inertial and satellite navigation, as well as for engineers involved in the practical implementation of integrated GNSS/INS systems.
... SINS is a completely self-contained navigation system without receiving or broadcasting any signals. SINS processes the angular rates and acceleration measurements from the gyroscopes and accelerometers, and then provides continuous positioning, velocity and attitude (PVA) information [11][12][13]. Compared with GNSS, the SINS works in almost all environments. ...
... The centralized integration filter (CIF) architecture is presented in Figure 1; all the measurements are directly employed to construct the measurement vector. The following Equations (11) and (12) illustrate the measurement vector of the CIF. Notably, the error state model of the CIF method remains the same as in Equation (2): ...
... where the superscript r i refers to the ith receiver; the subscript k + 1 refers to the epoch; Z r i k+1 is the measurement vector composed of the different information from the ith receiver and SINS; L The centralized integration filter (CIF) architecture is presented in Figure 1; all the measurements are directly employed to construct the measurement vector. The following Equations (11) and (12) illustrate the measurement vector of the CIF. Notably, the error state model of the CIF method remains the same as in Equation (2): ...
GNSS (global navigation satellite system) and SINS (strap-down inertial navigation system) integrated navigation systems have been the apparatus for providing reliable and stable position and velocity information (PV). Commonly, there are two solutions to improve the GNSS/SINS integration navigation system accuracy, i.e., employing GNSS with higher position accuracy in the integration system or utilizing the high-grade inertial measurement unit (IMU) to construct the integration system. However, technologies such as RTK (real-time kinematic) and PPP (precise point positioning) that improve GNSS positioning accuracy have higher costs and they cannot work under high dynamic environments. Also, an IMU with high accuracy will lead to a higher cost and larger volume, therefore, a low-cost method to enhance the GNSS/SINS integration accuracy is of great significance. In this paper, multiple receivers based on the GNSS/SINS integrated navigation system are proposed with the aim of providing more precise PV information. Since the chip-scale receivers are cheap, the deployment of multiple receivers in the GNSS/SINS integration will not significantly increase the cost. In addition, two different filtering methods with central and cascaded structure are employed to process the multiple receivers and SINS integration. In the centralized integration filter method, measurements from multiple receivers are directly processed to estimate the SINS errors state vectors. However, the computation load increases heavily due to the rising dimension of the measurement vector. Therefore, a cascaded integration filter structure is also employed to distribute the processing of the multiple receiver and SINS integration. In the cascaded processing method, each receiver is regarded as an individual “sensor”, and a standard federated Kalman filter (FKF) is implemented to obtain an optimal estimation of the navigation solutions. In this paper, a simulation and a field tests are carried out to assess the influence of the number of receivers on the PV accuracy. A detailed analysis of these position and velocity results is presented and the improvements in the PV accuracy demonstrate the effectiveness of the proposed method.
... To obtain the specific position of the UAV, the most common method is receiving signals from GNSS, such as the GPS of the United States and the BeiDou Navigation Satellite System of China, and performing the calculation to obtain its current position, which can achieve meter-level positioning accuracy [6][7][8][9][10][11][12][13]. However, due to environmental factors, the GNSS positioning signal can be interrupted or deviate when the UAV approaches landmarks such as high-rise buildings and electrical towers [14][15][16], making the GNSS signal unable to provide stable and accurate positioning information for the UAV. The main causes of GNSS signal outage and degradation in flight include antenna obscuration, multipath, fading due to adverse geometry, and Doppler shift. ...
... In order to complete navigation under the circumstance where the GNSS signal is lost, many international strategies have been proposed, including GPS/INS integrated navigation [16][17][18], anti-interference design of GNSS equipment [19], and additional auxiliary sensors such as optical flow [20], radar [21,22], and other sensors for navigation assistance. With the development of machine vision systems in recent years, it has become possible to correct the flight path through machine vision [23][24][25][26][27][28][29][30][31][32][33][34]. ...
GNSS information is vulnerable to external interference and causes failure when unmanned aerial vehicles (UAVs) are in a fully autonomous flight in complex environments such as high-rise parks and dense forests. This paper presents a pan-tilt-based visual servoing (PBVS) method for obtaining world coordinate information. The system is equipped with an inertial measurement unit (IMU), an air pressure sensor, a magnetometer, and a pan-tilt-zoom (PTZ) camera. In this paper, we explain the physical model and the application method of the PBVS system, which can be briefly summarized as follows. We track the operation target with a UAV carrying a camera and output the information about the UAV’s position and the angle between the PTZ and the anchor point. In this way, we can obtain the current absolute position information of the UAV with its absolute altitude collected by the height sensing unit and absolute geographic coordinate information and altitude information of the tracked target. We set up an actual UAV experimental environment. To meet the calculation requirements, some sensor data will be sent to the cloud through the network. Through the field tests, it can be concluded that the systematic deviation of the overall solution is less than the error of GNSS sensor equipment, and it can provide navigation coordinate information for the UAV in complex environments. Compared with traditional visual navigation systems, our scheme has the advantage of obtaining absolute, continuous, accurate, and efficient navigation information at a short distance (within 15 m from the target). This system can be used in scenarios that require autonomous cruise, such as self-powered inspections of UAVs, patrols in parks, etc.
... Even with the emergence of new kinds of aerial platforms, such as omnidirectional drones for manipulation, quadrotors have proved their reliability this last decade (Tognon and Franchi, 2018),, (Bodie et al., 2019). A quadrotor with suspended load is comparable to helicopters achieving object transport but with a difference in agility and sensory system (Wendel et al., 2006). Nevertheless, research tends to increase flight accuracy by embedding as many sensors as possible at the expense of lightness and agility (Lanegger et al.,2 06), (Panetsos et al., 2022). ...
... Fusing GNSS and INS using Kalman filters with heuristic propagation and update models has been ex- tensively explored and implemented commercially, for decades [48][16] [49][50] [51]. The INS runs faster than GPS measurement updates, allowing agile navigation of fastmoving objects such as quadrotors [52]. The drift in INS trajectory is intermittently corrected by GNSS measurements [50], while the INS takes full control during GPS outage (e.g., inside tunnels). ...
Precision agricultural robots require high-resolution navigation solutions. In this paper, we introduce a robust neural-inertial sequence learning approach to track such robots with ultra-intermittent GNSS updates. First, we propose an ultra-lightweight neural-Kalman filter that can track agricultural robots within 1.4 m (1.4-5.8× better than competing techniques), while tracking within 2.75 m with 20 mins of GPS outage. Second, we introduce a user-friendly video-processing toolbox to generate high-resolution (±5 cm) position data for fine-tuning pre-trained neural-inertial models in the field. Third, we introduce the first and largest (6.5 hours, 4.5 km, 3 phases) public neural-inertial navigation dataset for precision agricultural robots. The dataset, toolbox, and code are available at: https://github.com/nesl/agrobot.
... The MEMS sensors are widely used to measure the frequency, amplitude (strength) and spectrum (signature) of vibrations [1,2] enabling the ability to perform active monitoring of moving objects such as unmanned autonomous vehicles [3,4]. Vibrations have an influence on vehicle constancy and convenience of use, they also can be very dangerous for users. ...
The current paper describes the information and communication system, based on micromechanical inertial sensors (MEMS) to measure the dynamic response and status of the vehicle suspension elements. It consists of an inertial sensor network from at least two sensors, which are situated on the moving elements of the vehicle suspension. The communication system part reads and stores the inertial sensor data while the information system part calculates the frequency response, attenuation time, resonance frequencies and distance between moving parts. The calculated distance is compared with the adjusted clearance and the system accuracy is shown. It is shown that the system is capable to measure the distances from 0.6 to 1.0mm with 0.1mm accuracy. The inertial data scanning is performed with a sampling frequency of 160Hz, according to the expected peak accelerations and translations.
... The integration of GNSS and IMU data has been widely studied, especially in vehicle navigation [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. GNSS/IMU integration architectures can be categorized into loosely-coupled, tightly-coupled, and deep [46]. ...
This study presents the filter design of GNSS/IMU integration for wearable EPTS (Electronic Performance and Tracking System) of football players. EPTS has been widely used in sports fields recently, and GNSS (Global Navigation Satellite System) and IMU (Inertial Measurement Unit) in wearable EPTS have been used to measure and provide players’ athletic performance data. A sensor fusion technique can be used to provide high-quality analysis data of athletic performance. For this reason, the integration filter of GNSS data and IMU data is designed in this study. The loosely-coupled strategy is considered to integrate GNSS and IMU data considering the specification of the wearable EPTS product. Quaternion is used to estimate a player’s attitude to avoid the gimbal lock singularity in this study. Experiment results validate the performance of the proposed GNSS/IMU loosely-coupled integration filter for wearable EPTS of football players.
... External sensor aiding refers to the physical sensor itself, which provides external measurements to aid the INS. Radio frequency (RF) navigation, for example, offers GNSS broadcasting for outdoors [12]- [14], RF-based technologies [15]- [17] and Wi-Fi [18]- [20] for indoors. Given a GNSS-denied environment, acoustic navigation can be used, especially underwater, using sonar [21]- [23], range-based localization techniques [24]- [26], long and short baseline systems [27]- [30], and a Doppler velocity log (DVL) [31]- [33]. ...
The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.
... Among UAV navigation solutions, GPS and inertial measurement unit (IMU) aided inertial navigation systems (INS) are at the forefront [16], [17], [18], [19]. These systems use inertial data (accelerometer and gyroscope) and GPS pseudo-range measurements to estimate the platform states [20]. ...
This paper presents a comprehensive review of state-of-the-art navigation methods available for unmanned aerial vehicles (UAVs) used in parcel delivery. Particularly, the paper focuses on state-of-the-art sensor configurations, multi-sensor data fusion architectures, and their performance when employed for UAV navigation. Additionally, this paper presents the associated safety regulations for UAV navigation currently imposed by regulatory bodies in US and Canada. The existing navigation solutions sometimes produce degenerative results due to GPS loss, multipath signals, spoofing events, and other sensor degradation scenarios. Therefore, this article investigates the suitability of integrating visual lidar odometry and mapping (VLOAM) with GPS to overcome the limitations of existing navigation solutions. A comparative study of the multi-sensory combined solutions is presented with numerical simulations, validating the regulatory compliance of VLOAM and GPS integrated system under common GPS failure cases.
Note to Practitioners
—This work was motivated by the need for a survey on existing UAV navigation methods for parcel delivery applications. Different UAV navigation methods exist, depending on the sensors used and the sensor fusion architectures, with varying degrees of localization accuracy. It can be challenging for researchers and practitioners to decide which method to adopt for their application while complying with the existing safety regulations. Therefore, this paper presents an overview of the current safety regulation for UAV navigation and evaluates the state-of-the-art navigation methods against regulatory safety compliance. Additionally, a numerically validated safe navigation method is suggested for UAV-based parcel delivery. This paper provides researchers and practitioners with comprehensive reference sources in the UAV navigation field, which can help them develop suitable solutions to ensure safe navigation.
... Moreover, an IMU integrated with a vision unit can supply pose (i.e., attitude and position) information of a vehicle navigating with six degrees of freedom (6 DoF) [14]. Widely used methods of pose estimation include Gaussian filters [15], [16] and nonlinear filters [14], [17]. Nonetheless, pose estimation solutions in [14]- [17] produce good results only given the availability of linear velocity measurements obtained, for instance, by the Global Positioning System (GPS). ...
This paper proposes a novel observer-based controller for Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV) designed to directly receive measurements from a Vision-Aided Inertial Navigation System (VA-INS) and produce the required thrust and rotational torque inputs. The VA-INS is composed of a vision unit (monocular or stereo camera) and a typical low-cost 6-axis Inertial Measurement Unit (IMU) equipped with an accelerometer and a gyroscope. A major benefit of this approach is its applicability for environments where the Global Positioning System (GPS) is inaccessible. The proposed VTOL-UAV observer utilizes IMU and feature measurements to accurately estimate attitude (orientation), gyroscope bias, position, and linear velocity. Ability to use VA-INS measurements directly makes the proposed observer design more computationally efficient as it obviates the need for attitude and position reconstruction. Once the motion components are estimated, the observer-based controller is used to control the VTOL-UAV attitude, angular velocity, position, and linear velocity guiding the vehicle along the desired trajectory in six degrees of freedom (6 DoF). The closed-loop estimation and the control errors of the observer-based controller are proven to be exponentially stable starting from almost any initial condition. To achieve global and unique VTOL-UAV representation in 6 DoF, the proposed approach is posed on the Lie Group and the design in unit-quaternion is presented. Although the proposed approach is described in a continuous form, the discrete version is provided and tested. Keywords: Vision-aided inertial navigation system, unmanned aerial vehicle, vertical take-off and landing, observer-based controller algorithm, landmark measurement, exponential stability.
... The usage of the MEMS gyroscope has increased enormously over the last 20 years. These sensors have been extensively used in smart devices, automotive industries, household applications, aerospace, military applications, and so on [9,10]. The research on the MEMS vibratory gyroscopes started gaining maturity and moved towards practical designs at the start of the 21st century. ...
Micro-electromechanical systems (MEMS) vibrating gyroscopes have gained a lot of attention over the last two decades because of their low power consumption, easy integration, and low fabrication cost. The usage of the gyroscope equipped with an inertial measurement unit has increased tremendously, with applications ranging from household devices to smart electronics to military equipment. However, reliability issues are still a concern when operating this inertial sensor in harsh environments, such as to control the movement and alignment of mini-satellites in space, tracking firefighters at an elevated temperature, and assisting aircraft navigation in gusty turbulent air. This review paper focuses on the key fundamentals of the MEMS vibrating gyroscopes, first discussing popular designs including the tuning fork, gimbal, vibrating ring, and multi-axis gyroscopes. It further investigates how bias stability, angle random walk, scale factor, and other performance parameters are affected in harsh environments and then discusses the reliability issues of the gyroscopes.
... The multi-rotor helicopter also called multicopter is widely spread for the less mechanical parts, electrically controllable and small space of the take-off and landing, and is useful for aerial, observation and research applications [1]. However, the time-of-flight and cruising distance is limited by the battery capacity. ...
The fixed-wing aircraft has an advantage of wide range of flight area caused by the high-speed and the energy efficiency than the rotorcraft, but it cannot hover in the air and takes a runway field to take-off and landing. In our previous research, variable pitch wing attached multicopter is developed and the lift of wing during level flight is partially supported the weight of rotorcraft and is effective to reduce the power consumption. In this study, the trial production of a new concept vertical take-off and landing aircraft (VTOL) of tilted quad rotor attached flying wing was carried out for power saving and long flight. The airframe is made reinforced Styrofoam, and the 45° tilted quad rotors are fixed at a position symmetrical to the center of gravity. Stabilization during flight can be realized by a commercially available flight controller (FC) with custom settings. The FC is attached to the aircraft at an angle of 45°. The aircraft can be hover at the pitch angle of 45°. Continuous transition from hovering to level flight can be done with only elevator operation. The attitude of the aircraft changes to tilt forward of the FC, the aircraft flies horizontally as a fixed-wing aircraft. The attitude is controlled by the elevons rather than the thrust of the rotors. The lift of wing during level flight caused the reduction of power consumption down to 19% compared with that of hovering.
... Several spoofing detection schemes require extra peripherals like multiple antennas [22], [53], [54], which detect discrepancies in the angle of arrival of GPS signals. GPS signals and location estimates are correlated with data from extra IMU sensors [33], [42], [74], [76] for detecting GPS spoofing attacks using vector-based tracking. Extensive work is present that focuses on the use of EKF to aid in recovering from GPS glitches [40], [72]. ...
... The spray system was controlled by an Arduino Zero microcontroller [21,22] , which is a convenient and easy-to-use open-source electronic control platform. The altitude was measured using a light detection and ranging (LiDAR) sensor, and video data were acquired during flight tests to compensate for the low accuracy of global positioning system (GPS) data [23,24] . All flights were controlled by a DX7 transmitter (Spektrum, Champaign, IL, USA) and lateral position was estimated using the video data acquired during the flights. ...
... With the rapid development of MEMS technology, research on inertial devices such as accelerometers and gyroscopes has become a hotspot. (1)(2)(3)(4)(5) MEMS gyroscopes are widely used in aviation, aerospace, and other high-precision measurement and control fields due to their low cost, low power consumption, robustness, and excellent performance. (6,7) Unfortunately, the performance of MEMS gyroscopes is limited by many factors, such as background noise, humidity drift, and temperature drift, which has greatly inhibited their development. ...
The output of a MEMS gyroscope is easily influenced by temperature, which has led to a bottleneck in the development of gyroscopes. Therefore, to eliminate the temperature error of gyroscopes, a parallel processing algorithm based on variational modal decomposition optimized by genetic particle swarm optimization variational modal decomposition (GPSO-VMD) and an improved backpropagation (BP) neural network is proposed in this paper. First, for the original output signal of a gyroscope, GPSO is adopted to search for the optimal parameters for VMD. Next, the optimal parameters (kbest, αbest) are applied to VMD to obtain intrinsic mode functions (IMFs). Then, according to the calculated result of multiscale permutation entropy (MPE), IMFs are divided into three categories: noise items, mixed items, and drift items. The three categories are treated separately: noise items are removed directly, mixed items are filtered, and for drift items, temperature errors are eliminated by using an improved BP neural network. The final signal is then obtained through reconstruction. Compared with the traditional optimization algorithm, GPSO has excellent global search ability and strong convergence. The BP neural network improved by the genetic algorithm (GA) overcomes the problem of easily falling into a local optimum, and excellent prediction performance is achieved. Experimental results demonstrate the feasibility of this proposed hybrid model in eliminating gyroscope temperature errors. © 2021 M Y U Scientific Publishing Division. All rights reserved.
... In general, quadrotors make use of GPS and inertial measurement unit (IMU) system for positioning [20,21]. However, GPS and IMU are not suitable for high-accuracy applications for two reasons: (1) GPS positioning accuracy is sensitive to atmospheric (ionosphere and troposphere) delay, ephemeris error, clock bias, and multi-path effects; (2) The choice of IMU is limited by the weight and size of UAVs. ...
Unmanned aerial vehicle (UAV) light shows (UAV-LS) have a wow factor due to their advantages in terms of environment friendliness and controllability compared to traditional fireworks. In this paper, a UAV-LS system is developed including a collision-free formation transformation trajectory planning algorithm, a software package that facilitates animation design and real-time monitoring and control, and hardware design and realization. In particular, a dynamic task assignment algorithm based on graph theory is proposed to reduce the impact of UAV collision avoidance on task assignment and the frequency of task assignment in the formation transformation. In addition, the software package consists of an animation interface for formation drawing and 3D animation simulation , which helps the monitoring and control of UAVs through a real-time monitoring application. The developed UAV-LS system hardware consists of subsystems of decision-making, real-time kine-matic (RTK) global positioning system (GPS), wireless communication, and UAV platforms. Outdoor experiments using six quadrotors are performed and details of implementations of high-accuracy positioning, communication, and computation are presented. Results show that the developed UAV-LS system can successfully complete a light show and the proposed task assignment algorithm performs better than traditional static ones.
... Furthermore, after combining the GNSS with real-time kinematics (RTK) [10], the GNSS/MEMS-INS integrated navigation system can still have sub-meter navigation accuracy when the GNSS signal outages in a short time. Therefore, it is very suitable for the application of vehicle navigation and wearable equipment, such as crewless micro aerial vehicles [11], land vehicle navigation (LVN) [12][13][14][15], mobile mapping systems [16], wearable sports equipment [17,18], and pedestrians [19,20]. In all these applications, an important common problem is the positioning accuracy and the power consumption performance of GNSS/MEMS-INS during the period of GNSS signal or not, which often occurs in different situations, such as boulevard, tunnels, overhead bridges, and urban streets. ...
Positioning accuracy and power consumption are essential performance indicators of integrated navigation and positioning chips. This paper proposes a single-frequency GNSS/MEMS-IMU/odometer real-time high-precision integrated navigation algorithm with dynamic power adaptive adjustment capability in complex environments. It is implemented in a multi-sensor fusion navigation SiP (system in package) chip. The simplified INS algorithm and the simplified Kalman filter algorithm are adopted to reduce the computation load, and the strategy of adaptively adjusting the data rate and selecting the observation information for measurement update in different scenes and motion modes is combined to realize high-precision positioning and low power consumption in complex scenes. The performance of the algorithm is verified by real-time vehicle experiments in a variety of complex urban environments. The results show that the RMS statistical value of the overall positioning error in the entire road section is 0.312 m, and the overall average power consumption is 141 mW, which meets the requirements of real-time integrated navigation for high-precision positioning and low power consumption. It supports single-frequency GNSS/MEMS-IMU/odometer integrated navigation SiP chip in real-time, high-precision, low-power, and small-volume applications.
... in a land vehicle navigation [40] and in an autonomous helicopter by Wendel et al. [41]. ...
With the development of micro-electro-mechanical system (MEMS) technologies, emerging MEMS applications such as in-situ MEMS IMU calibration, medical imaging via endomicroscopy, and feedback control for nano-positioning and laser scanning impose needs for especially accurate measurements of motion using on-chip sensors. Due to their advantages of simple fabrication and integration within system level architectures, capacitive sensors are a primary choice for motion tracking in those applications. However, challenges arise as often the capacitive sensing scheme in those applications is unconventional due to the nature of the application and/or the design and fabrication restrictions imposed, and MEMS sensors are traditionally susceptible to accuracy errors, as from nonlinear sensor behavior, gain and bias drift, feedthrough disturbances, etc. Those challenges prevent traditional sensing and estimation techniques from fulfilling the accuracy requirements of the candidate applications. The goal of this dissertation is to provide a framework for such MEMS devices to achieve high-accuracy motion estimation, and specifically to focus on innovative sensing and estimation techniques that leverage unconventional capacitive sensing schemes to improve estimation accuracy. Several research studies with this specific aim have been conducted, and the methodologies, results and findings are presented in the context of three applications. The general procedure of the study includes proposing and devising the capacitive sensing scheme, deriving a sensor model based on first principles of capacitor configuration and sensing circuit, analyzing the sensor’s characteristics in simulation with tuning of key parameters, conducting experimental investigations by constructing testbeds and identifying actuation and sensing models, formulating estimation schemes is to include identified actuation dynamics and sensor models, and validating the estimation schemes and evaluating their performance against ground truth measurements. The studies show that the proposed techniques are valid and effective, as the estimation schemes adopted either fulfill the requirements imposed or improve the overall estimation performance. Highlighted results presented in this dissertation include a scale factor calibration accuracy of 286 ppm for a MEMS gyroscope (Chapter 3), an improvement of 15.1% of angular displacement estimation accuracy by adopting a threshold sensing technique for a scanning micro-mirror (Chapter 4), and a phase shift prediction error of 0.39 degree for a electrostatic micro-scanner using shared electrodes for actuation and sensing (Chapter 5).
... Several spoofing detection schemes require extra peripherals like multiple antennas [20,44,45], which detect discrepancies in the angle of arrival of GPS signals. GPS signals and location estimates are correlated with data from extra IMU sensors [28,35,64,66] for detecting GPS spoofing attacks using vector-based tracking. Extensive work is present that focuses on the use of EKF to aid in recovering from GPS glitches [33,62]. ...
It is well-known that GPS is vulnerable to signal spoofing attacks. Although several spoofing detection techniques exist, they are incapable of mitigation and recovery from stealthy attackers. In this work, we present SemperFi, a single antenna GPS receiver capable of tracking legitimate GPS satellite signals and estimating the true location even during a spoofing attack. The main challenge in building SemperFi is, unlike most wireless systems where \emph{the data} contained in the wireless signals is important, GPS relies on the time of arrival (ToA) of satellite signals. SemperFi is capable of distinguishing spoofing signals and recovering legitimate GPS signals that are even completely overshadowed by a strong adversary. We exploit the short-term stability of inertial sensors to identify the spoofing signal and extend the successive interference cancellation algorithm to preserve the legitimate signal's ToA. We implement SemperFi in GNSS-SDR, an open-source software-defined GNSS receiver, and evaluate its performance using UAV simulators, real drones, a variety of real-world GPS datasets, and various embedded platforms. Our evaluation results indicate that in many scenarios, SemperFi can identify adversarial peaks by executing flight patterns that are less than 50 m long and recover the true location within 10 seconds (Jetson Xavier). We show that our receiver is secure against stealthy attackers who exploit inertial sensor errors and execute seamless takeover attacks. We design SemperFi as a pluggable module capable of generating a spoofer-free GPS signal for processing on any commercial-off-the-shelf GPS receiver available today. Finally, we release our implementation to the community for usage and further research.
... With the development of Micro-Electro-Mechanical System (MEMS) technology, the MEMS inertial measurement unit (MIMU) is gaining attention due to its integrability and miniaturization in several fields such as guided weaponry, unmanned automatic vehicles (UAVs), and robots [1][2][3][4][5][6]. As a dead-reckoning method, the initial alignment accuracy has a vital impact to the final inertial navigation results, while there are only tens of seconds for a tactical weapon. ...
This paper presents a novel multiple strong tracking adaptive square-root cubature Kalman filter (MSTASCKF) based on the frame of the Sage–Husa filter, employing the multi-fading factor which could automatically adjust the Q value according to the rapidly changing noise in the flight process. This filter can estimate the system noise in real-time during the filtering process and adjust the system noise variance matrix Q so that the filtering accuracy is not significantly reduced with the noise. At the same time, the residual error in the filtering process is used as a measure of the filtering effect, and a multiple fading factor is introduced to adjust the posterior error variance matrix in the filtering process, so that the residual error is always orthogonal and the stability of the filtering is maintained. Finally, a vibration test is designed which simulates the random noise of the short-range guided weapon in flight through the shaking table and adds the noise to the present simulation trajectory for semi-physical simulation. The simulation results show that the proposed filter can significantly reduce the attitude estimation error caused by random vibration.
... In [236], the state variables (position, velocities, and attitude) of an autonomous quadrotor UAV were estimated by using the Kalman Filter (KF) under the condition of sufficient measurements. In [66], the quadrotor's attitude was estimated by using two Extended Kalman Filters (EKF) along with a Direction Cosine Matrix (DCM) algorithm for a single low-cost IMU sensor. ...
The objective of this thesis is to realize the modeling, trajectory planning, and control of an unmanned helicopter robot for monitoring large areas, especially in precision agriculture applications. Several tasks in precision agriculture are addressed. In pest surveillance missions, drones will be equipped with specialized cameras. A trajectory will be researched and created to enable unmanned aircraft to capture images of entire crop areas and avoid obstacles during flight. Infected areas will be then identified by analyzing taken images. In insecticides spraying, the aircraft must be controlled to fly in a pre-programmed trajectory and spray the insecticide over all the infected crop areas.In the first part, we present a new complete coverage path planning algorithm by proposing a new cellular decomposition which is based on a generalization of the Boustrophedon variant, using Morse functions, with an extension of the representation of the critical points. This extension leads to a reduced number of cells after decomposition. Genetic Algorithm (GA) and Travelling Salesman Problem (TSP) algorithm are then applied to obtain the shortest path for complete coverage. Next, from the information on the map regarding the coordinates of the obstacles, non-infected areas, and infected areas, the infected areas are divided into several non-overlapping regions by using a clustering technique. Then an algorithm is proposed for generating the best path for a Unmanned Aerial Vehicle (UAV) to distribute medicine to all the infected areas of an agriculture environment which contains non-convex obstacles, pest-free areas, and pests-ridden areas.In the second part, we study the design of a robust control system that allows the vehicle to track the predefined trajectory for a dynamic model-changing helicopter due to the changes of dynamic coefficients such as the mass and moments of inertia. Therefore, the robust observer and control laws are required to adopt the changes in dynamic parameters as well as the impact of external forces. The proposed approach is to explore the modeling techniques, planning, and control by the Takagi-Sugeno type technique. To have easily implantable algorithms and adaptable to changes in parameters and conditions of use, we favor the synthesis of Linear Parameter Varying (LPV) Unknown Input Observer (UIO), LPV quadratic state feedback, robust state feedback, and static output feedback controllers. The observer and controllers are designed by solving a set of Linear Matrix Inequality (LMI) obtained from the Bounded Real Lemma and LMI regions characterization.Finally, to highlight the performances of the path planning algorithms and generated control laws, we perform a series of simulations in MATLAB Simulink. Simulation results are quite promising. The coverage path planning algorithm suggests that the generated trajectory shortens the flight distance of the aircraft but still avoids obstacles and covers the entire area of interest. Simulations for the LPV UIO and LPV controllers are conducted with the cases that the mass and moments of inertias change abruptly and slowly. The LPV UIO is able to estimate state variables and the unknown disturbances and the estimated values converge to the true values of the state variables and the unknown disturbances asymptotically. The LPV controllers work well for various reference signals (impulse, random, constant, and sine) and several types of disturbances (impulse, random, constant, and sine).
... To estimate the UAV group's global pose, at least one UAV in the group should be able to localize itself with respect to global references. Traditionally, Global Navigation Satellite System (GNSS) and IMUs are integrated to provide vehicles' global pose estimates [15]. However, GNSS is not always available or reliable-due to reasons such as signal blockages, multipath reflection, and jamming. ...
The problem of cooperative localization for a small group of Unmanned Aerial Vehicles (UAVs) in a GNSS denied environment is addressed in this paper. The presented approach contains two sequential steps: first, an algorithm called cooperative ranging localization, formulated as an Extended Kalman Filter (EKF), estimates each UAV's relative pose inside the group using inter-vehicle ranging measurements; second, an algorithm named cooperative magnetic localization, formulated as a particle filter, estimates each UAV's global pose through matching the group's magnetic anomaly measurements to a given magnetic anomaly map. In this study, each UAV is assumed to only perform a ranging measurement and data exchange with one other UAV at any point in time. A simulator is developed to evaluate the algorithms with magnetic anomaly maps acquired from airborne geophysical survey. The simulation results show that the average estimated position error of a group of 16 UAVs is approximately 20 meters after flying about 180 kilometers in 1 hour. Sensitivity analysis shows that the algorithms can tolerate large variations of velocity, yaw rate, and magnetic anomaly measurement noises. Additionally, the UAV group shows improved position estimation robustness with both high and low resolution maps as more UAVs are added into the group.
... Nowadays, aerial photography and more specifically unmanned aerial vehicles (UAVs) extended with multispectral cameras (also called Unmanned Aerial Systems, UASs) are becoming more numerous and affordable each year giving access to temporal and spatial resolutions not seen before (Wendel et al., 2006). UAVs have long been used in various, but mostly in military applications in the past. ...
In practice, the drone may not always be able to follow a pre-planned flight path.
Therefore, producing orthophotos can be problematic. The drone's shooting positions can
be read from the captured images, so the actual flight path is known. The recorded route
data and camera properties can be used to calculate the photographed area. It is a
prerequisite for making an orthophoto that the adjacent images overlap a certain
percentage. Calculating the area of an overlapping rectangle is lengthy by specifying the
coordinates of the intersecting sides. We have developed a simpler method for this. One
of the two overlapping rectangles was fixed and examined to determine the curve of the
center of the other rectangle such that the overlapping area was exactly equal to the
required overlap. This curve has become a hyperbola arc. This makes the calculation
simpler and can be traced back to comparing the lengths of two sections. The method was
developed using GeoGebra software.
... Nowadays, aerial photography and more specifically unmanned aerial vehicles (UAVs) extended with multispectral cameras (also called Unmanned Aerial Systems, UASs) are becoming more numerous and affordable each year giving access to temporal and spatial resolutions not seen before (Wendel et al., 2006). UAVs have long been used in various, but mostly in military applications in the past. ...
Partitioning clustering has been one of the key components of data analytics to discover meaningful patterns in agricultural big data, driven by the increasing use of IoT-based technologies in smart farming. In partitioning clustering, the quality of clustering or performances of clustering algorithms are mostly evaluated by using the internal validity indices. In this study, the effectiveness of some widely used internal fuzzy indices are compared using the basic Fuzzy C-Means clustering algorithm. It is especially aimed to investigate changes in the effectiveness of validity indices when fuzzy data points are at different distances from the cluster centers. According to the results obtained on the simulated two-dimensional datasets, Fuzzy Silhouette, Fuzzy Hypervolume and Kwon are the most successful indices in validation of fuzzy clustering results.
See the paper at http://journal.magisz.org/files/journals/JAI_Vol_10_No_2.pdf
... Sensors 2020, 20, 538 2 of 17 and accelerometers) and magnetic sensors, within Extended Kalman Filters [13,14] or, more recently, Particle Filters [15], and using either loosely-or tightly-coupled architectures [16]. In this respect, since onboard gyros do not have enough sensitivity to measure the Earth rate vector, magnetometers play a key role, despite being typically characterized by low bandwidth and high measurement noise. ...
This paper presents a new method to improve the accuracy in the heading angle estimate provided by low-cost magnetometers on board of small Unmanned Aerial Vehicles (UAVs). This task can be achieved by estimating the systematic error produced by the magnetic fields generated by onboard electric equipment. To this aim, calibration data must be collected in flight when, for instance, the level of thrust provided by the electric engines (and, consequently, the associated magnetic disturbance) is the same as the one occurring during nominal flight operations. The UAV whose magnetometers need to be calibrated (chief) must be able to detect and track a cooperative vehicle (deputy) using a visual camera, while flying under nominal GNSS coverage to enable relative positioning. The magnetic biases’ determination problem can be formulated as a system of non-linear equations by exploiting the acquired visual and GNSS data. The calibration can be carried out either off-line, using the data collected in flight (as done in this paper), or directly on board, i.e., in real time. Clearly, in the latter case, the two UAVs should rely on a communication link to exchange navigation data. Performance assessment is carried out by conducting multiple experimental flight tests.
... [5], [7], [8], [14], [17] روش [16], [18], [21], [23], [24], [27], [29] ...
... At present, the traditional high-fixing schemes include GPS, barometer and ultrasonic wave [1][2][3]. These traditional height-definition schemes have many limitations, and the consequences include leakage and re-spray to influence the spray effect in the operation by plant protection UAV, which 2 even lead to explosion accident due to height drop. ...
When the plant protection UAV on farmland spraying, the UAV need to always maintain a relative height of 1-2 m away from the crops to ensure uniform and efficient spraying of pesticides. At present, the commonly used high-fixing schemes, such as GPS, barometer, ultrasound and so on, there are some deficiencies. Aiming at this problem, a method of reconstructing 3D terrain of farmland by UAV electro-optical payload taking sequence images is proposed, which can achieve the goal of UAV height-fixing. Firstly, the feature points selection module is established. The SILC image segmentation algorithm is adopted to segment the image and the centroid points of all the superpixel blocks are calculated. Then, the corner points are extracted by using Harris corner detector and these two parts of points are mixed together as the feature points. Secondly, the feature point matching module based on SAD algorithm is proposed. Finally, a three-dimensional reconstruction algorithm according to monocular sequence images, UAV position, attitude and camera pointing angle is derived. The results of the experiments demonstrated that this method can quickly and effectively reconstruct farmland terrain, which can provide a data base for UAV high-fixing and has important application value.
This study utilizes a low-cost, low-power, and highly accurate monocular visual-inertial odometry (VIO) as the navigation algorithm for unmanned aerial vehicles (UAV). However, VIO may experience positioning inaccuracies or even loss in cases of lighting changes and insufficient environmental textures. To mitigate the accumulation of errors in the algorithm, global localization information is introduced. In order to address the issue of positioning accuracy in the absence of Global Navigation Satellite Systems (GNSS), this paper proposes an optimized Visual-Inertial Navigation System (VINS) based on landmark recognition, considering the prior information of UAV flight missions. The system relies solely on visual and IMU information, employing the VINS-Mono algorithm for high-precision positioning. After flying a certain distance, the system recognizes landmarks from a landmark database and obtains their prior coordinate information. Finally, the pose graph and optimization algorithm are used to refine the pose of all keyframes. Experimental results indicate that this navigation system effectively reduces the accumulated errors of VINS and enhances the positioning accuracy in scenarios where GNSS signals are completely unavailable.
The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided inertial navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.
Accurate identification of vehicle states and complex driving environment states is the necessary information base for i-EFV to carry out safe and energy-saving controls. For i-EFV that integrates complex electromechanical systems, faces time-varying traffic environment and implements multi-performance objective control, there are three main problems in the comprehensive identification vehicle states and environment information: (1) the large amount of information data obtained based on V2V communication, V2I communication, remote wireless communication and onboard sensing system is overlapping and redundant, which needs integrated analysis and processing to form a unified description of vehicle and environment; (2) the vehicle driving environment is complex and changeable, and the information obtained from existing sensing systems is seriously disturbed, which makes it necessary to obtain accurate object information based on the characteristics of redundancy of multi-source sensors; (3) the accurate information on the characteristics of driver-vehicle-road traffic environment cannot be obtained directly through the sensors, which makes it necessary to fuse multi-source information and conduct comprehensive analysis. Therefore, in order to effectively use the multi-source and redundant data information to accurately identify and predict vehicle state and traffic environment, a systematic signal processing method is required.
Precision during the guided terminal flight in short-range rockets is a key factor that must be improved using several methodologies. Traditionally, inertial navigation systems allowed to unbind the accuracy concerning the range. Unfortunately, in short-range rockets, the response of the inertial-based controller has to be fast enough to correct the trajectory in a short time. This fact implies that to achieve high precision, either the use of systems too large to be implemented in portable rockets is required, or systems that are too expensive concerning the total cost of the rocket. For these systems, artificial intelligence-based guidance tactics could improve the precision, since once the network is trained, it is not necessary to know the dynamics of the vehicle, and the network itself can react quickly, simplifying the system and reducing economic costs using relatively simple electronics. The neural networks are trained using a nonlinear-dynamics model based on a simulated flight-dynamics and verified with real flight data. The simulation results show that the proposed method works effectively in a six-degree-of-freedom simulation environment, with excellent accuracy and robustness to parameter uncertainty. The appropriateness of the closed-loop performance is validated using Monte Carlo analysis across a wide range of uncertainty scenarios.
Inertial measurement units (IMUs), composed of gyroscopes, accelerometers, and magnetometers, have been widely used in the fields of human motion animation, rehabilitation, robotics, and aerospace. However, their performances degenerate remarkably with external acceleration and magnetic disturbance. To handle this issue, we employ a multi-kernel maximum correntropy Kalman filter (MKMCKF) to suppress the adversarial acceleration and magnetic disturbance and use Bayesian optimization (BO) to explore the optimal kernel bandwidths. We validate our algorithm in a set of experiments with different levels of disturbance. Results show that the proposed method is significantly better than the traditional error state Kalman filter (ESKF) and the gradient descent (GD) method, and its root mean square error (RMSE) is less than
$0.4629^{\circ }$
on the roll and pitch even under the worst testing case.
Aiming at producing a high-quality flight path for the unmanned autonomous helicopter with multi-constraints, a path planning method is proposed based on the multi-strategy evolutionary learning artificial bee colony algorithm in this paper. Firstly, an evolutionary learning framework is established for the artificial bee colony algorithm based on brain-like cognition. By integrating the swarm intelligence and human cognitive mechanism, this framework gives more autonomy and intelligence to the bee colony. In addition, a multi-strategy evolutionary database is built based on the evolutionary learning framework to replace the traditional evolutionary approach of the artificial bee colony algorithm. Different nectar sources adopt different evolutionary strategies according to the integrated feedback mechanism, and evolutionary behavioral selection probability is updated through the accumulation of experience and the exploration of new knowledge. The simulation results show that the trajectories produced by the multi-strategy evolutionary learning artificial bee colony algorithm have better fuel economy and higher safety than other comparison algorithms, and the number of optimization iterations can be reduced by at least 12%.
This paper demonstrated the coupled surface effects of thermal Casimir force and squeeze film damping (SFD) on size-dependent electromechanical stability and bifurcation of torsion micromirror actuator. The governing equations of micromirror system are derived, and the pull-in voltage and critical tilting angle are obtained. Also, the twisting deformation of torsion nanobeam can be tuned by functionally graded carbon nanotubes reinforced composites (FG-CNTRC). A finite element analysis (FEA) model is established on the COMSOL Multiphysics platform, and the simulation of the effect of thermal Casimir force on pull-in instability is utilized to verify the present analytical model. The results indicate that the numerical results well agree with the theoretical results in this work and experimental data in the literature. Further, the influences of volume fraction and geometrical distribution of CNTs, thermal Casimir force, nonlocal parameter, and squeeze film damping on electrically actuated instability and free-standing behavior are detailedly discussed. Besides, the evolution of equilibrium states of micromirror system is investigated, and bifurcation diagrams and phase portraits including the periodic, homoclinic, and heteroclinic orbits are described as well. The results demonstrated that the amplitude of the tilting angle for FGX-CNTRC type micromirror attenuates slower than for FGO-CNTRC type, and the increment of CNTs volume ratio slows down the attenuation due to the stiffening effect. When considering squeeze film damping, the stable center point evolves into one focus point with homoclinic orbits, and the dynamic system maintains two unstable saddle points with the heteroclinic orbits due to the effect of thermal Casimir force.
High-throughput phenotyping has been widely studied in plant science to monitor plant growth and analyze the influence of genotypes and environment on plant growth. To meet the demand of large-scale high-throughput phenotyping, unmanned aerial vehicles (UAVs) have been developed for near-ground remote sensing. UAVs based remote sensing has been used for high-throughput phenotyping of various traits of plants. This review focused on the applications of UAVs based remote sensing of different traits with different phenotyping sensors. In this review, the UAVs platforms and the phenotyping sensors were briefly introduced. The applications of UAVs to obtain and analyze plant phenotype traits were introduced and summarized by the traits in a more comprehensive way. A comparison of different phenotyping sensors was conducted. Furthermore, the challenges and future prospects of phenotype information acquisition and data analysis using UAVs as remote sensing platforms were also discussed. Since the current studies from various countries and researchers were fragmented to just explore the feasibility of UAVs based high-throughput phenotyping, this review aimed to provide the researchers and readers the current applications of UAVs for high-throughput phenotyping and how the studies were conducted, provide guidelines for future studies.
Unmanned aircraft systems (UAS) generally use Global Navigation Satellite System (GNSS) measurements to estimate their state (position and orientation) for outdoor navigation. However, in urban environments, GNSS pseudorange measurements contain biases due to multipath effects and signal blockages by nearby buildings. For safe navigation in such environments, it is beneficial to predict the state uncertainty while accounting for the effect of measurement biases. Reachability analysis is a commonly used tool to predict the state uncertainty of a system. However, existing works do not account for the effect of measurement biases on state estimation, which consequently affects the predicted state uncertainty. Additionally, majority of the existing literature focuses on linear systems, whereas the dynamics of widely used practical systems are better captured by non-linear models. Thus, in this paper we present a non-linear stochastic reachability analysis to predict bounds on the state uncertainty while accounting for measurement biases. We derive the analysis for a fixed-wing UAS navigating using ranging measurements. In order to evaluate our predicted bounds for GNSS-based navigation, we simulate a 3D urban environment and the pseudorange biases due to multipath effects. We validate the predicted bounds for multiple trajectories in the simulated environment. Finally, we demonstrate the applicability of our predicted bounds towards ensuring safe UAS navigation in a shared airspace.
Considering the drawbacks that GPS signal is susceptible to obstacles and TAN becomes useless in some area when without any terrain data or with a featureless terrain field, to realize long-distance and high-precision navigation, a navigation system based on SINS/GPS/TAN/EOAN is presented. When GPS signal is available, GPS is used to correct errors of SINS; when GPS is unavailable, a terrain selection method based on the entropy weighted gray relational decision-making method is use to distinguish terrain into matchable areas and unmatchable areas; then, for the matchable areas, TAN is used to correct errors of SINS, for the unmatchable areas, EOAN is used to correct errors of SINS. The principles of SINS, GPS, TAN, and EOAN are analyzed, the mathematic models of SINS/GPS, SINS/TAN, and SINS/EOAN are constructed, and finally the federated Kalman filter is used to fuse navigation information. Simulation results show that the trajectory of SINS/GPS/TAN/EOAN is close to the ideal one in both matchable area or unmatchable area and whose navigation errors are obviously reduced, which is important for the realization of long-time and high-precision positioning.
The accuracy of pedestrian positioning is helpful to ensure the pedestrian safety in both indoor and outdoor environments. Improve the accuracy of pedestrian positioning is a key research issue. In order to solve the problem that the indoor and outdoor pedestrian navigation is not continuous and the accuracy is low, a pedestrian seamless navigation and positioning method based on BDS/GPS/IMU is proposed. In outdoor environment, in order to improve the availability of dynamic positioning when the single-system observation geometry is not ideal, the key techniques such as the differential coordinates and time benchmark in BDS/GPS positioning are studied, and a method of eliminating time difference by the independent combination difference in the system is proposed. This method simplifies the operation steps and overcomes the current compatible positioning difficulties without the time difference between BDS and GPS. It can be seen that the more the number of visible satellites, the better the space geometric distribution. In the combined positioning experiment results of BDS and GPS, the number of visual satellites are about 6–8 when GPS is used alone. When using the combined system, the number of visible satellites increased to about 16. The increase in the number of visible satellites greatly improves the observation geometry. For the Position Dilution of Precision (PDOP), the maximum PDOP of the dual system is 2.7, which is significantly lower than that of the single system, and the observation geometry performance is greatly improved. In the effective positioning time, for BDS/GPS, the positioning accuracy of elevation (U) direction is better than 4 cm, and the positioning accuracy of North (N) and East (E) direction is better than 2 cm. Based on the analysis of pedestrian gait characteristics, a multi-condition constrained zero-velocity detection algorithm is proposed. For the error of the inertial sensor error is accumulated over the time, the zero velocity update (ZUPT) algorithm is implemented to correct the cumulative errors by using to the designed extended Kalman filter (EKF) with the velocity and angular velocity information as the measurements. The results show that the accuracy of dual-mode positioning system of BDS compatible with GPS is better than the single-mode GPS positioning, the outdoor position accuracy can reach centimeter level, and under ZUPT compensation the indoor error ratio is 1%, which can achieve more accurate pedestrian seamless navigation.
From the early years of aviation, flying cars have constituted an appealing topic for science-fiction scenarios. Currently, recent technological developments demonstrate that flying cars will be introduced in the traffic fleet over the next few years. Despite their forthcoming penetration in the automobile market, the level of anticipated acceptance from the traveling population has not been investigated yet in travel demand literature. This study aims – for the first time to the authors' knowledge – to provide a preliminary investigation of individuals' perceptions and expectations towards the adoption of flying cars. For this purpose, 692 individuals were questioned in the context of an online survey about their willingness to pay for and willingness to use flying cars for various pricing and trip scenarios, as well as about the benefits and concerns that will arise from the introduction of flying cars in the traffic fleet. To understand the determinants of individuals' expectations, their willingness to pay for and use flying cars was statistically modeled, by employing a grouped random parameters bivariate probit framework, which accounts for multiple layers of unobserved heterogeneity in the respondent's decision-making process. The statistical analysis revealed that various individual-specific socio-demographic, behavioral and driving attributes, as well as individuals' attitudinal perspectives towards the cost, safety, security and environmental implications of the flying cars, affect their willingness to adopt this emerging transportation technology. Despite the current limited awareness about the operation of flying cars, the findings of this study can provide insights regarding critical challenges that should be addressed by policymakers, legislative companies, and manufacturing companies after the introduction of flying cars in the traffic fleet.
The problem of cooperative localization for a small group of unmanned aerial vehicles (UAVs) in a Global Navigation Satellite System-denied environment is addressed in this paper. The presented approach contains two sequential steps: first, an algorithm called cooperative ranging localization, formulated as an extended Kalman filter, estimates each UAV’s relative pose inside the group using intervehicle ranging measurements; second, an algorithm named cooperative magnetic localization, formulated as a particle filter, estimates each UAV’s global pose through matching the group’s magnetic anomaly measurements to a given magnetic anomaly map. In this study, each UAV is assumed to only perform a ranging measurement and data exchange with one other UAV at any point in time. A simulator is developed to evaluate the algorithms with magnetic anomaly maps acquired from airborne geophysical survey. The simulation results show that the average estimated position error of a group of 16 UAVs is approximately 20 m after flying about 180 km in 1 h. Sensitivity analysis shows that the algorithms can tolerate large variations of velocity, yaw rate, and magnetic anomaly measurement noises. Additionally, the UAV group shows improved position estimation robustness with both high- and low-resolution maps as more UAVs are added into the group.
The case of a land vehicle traveling in a straight and leveled trajectory with a position aided inertial navigation system (INS) is considered. In such a scenario, the heading angle and some of the inertial sensors error states are not observable. To circumvent this problem, we propose a method to extract attitude information from position measurements history provided by the external sensor. Simultaneously utilizing the attitude information and position updates in the navigation filter enables the estimation of the heading angle and all of the inertial sensors error states. That is, using the proposed approach the system becomes completely observable. Simulation and field experiment results are provided to show the navigation solution performance when applying the proposed approach.
Due to stochastic noises, modeling uncertainties and nonlinearities in low-cost inertial measurement units, the positioning error of strap-down inertial navigation systems are increased exponentially. So, inertial navigation system is integrated with aiding navigation systems like a global navigation satellite system by using an estimation algorithm to obtain an acceptable positioning accuracy. In urban area the global navigation satellite
system signal may be obstructed because of tall trees and buildings. Therefore, in the paper a novel constrained adaptive integration algorithm is developed for integration the strap-down inertial navigation system and global navigation satellite system. In this algorithm, the velocities constraints in body frame in addition to altitude constraints based on a barometer data are firstly developed, and then a constrained estimation algorithm is designed based on the proposed constraints. In addition a type-2 fuzzy algorithm is used to calculate the estimator parameters based on vehicle maneuvers. The real vehicular tests are used for implantation and validation of the proposed algorithm. The experimental results indicate that, the proposed adaptive constrained estimation algorithm enhanced the estimation accuracy of the strap-down inertial navigation system steady states.
In this paper, a novel algorithm for the real-time modeling of the inertial sensor errors is developed based on the genetic algorithm. In the inertial navigation systems, failure to compensate inertial sensor errors not only could result in exponentially increasing of positioning errors, but also leads to loss of precision in the estimation of other states. This subject indicates the importance of providing an accurate algorithm for sensor errors modeling in the Inertial Navigation Systems (INS). In this respect, a genetic algorithm is proposed in this study for modeling of inertial sensor errors. The main aim of designing this algorithm is to improve the estimation accuracy of position, velocity and attitude of the INS in the presence of uncertainties in inertial sensors. Some vehicular tests have been carried out in order to comparison, implementation and verification of the proposed integration scheme. The experimental results indicate that the proposed algorithm enhanced the estimation accuracy compared with the conventional Gauss-Markov model. Therefore, in general, the proposed method will improve the results of inertial navigation.
Abstract For flight control of helicopter UAVs, it is essential to have high-quality 6-DOF atti- tude measurements. In the course of the miniaturization, the need for reliable orientation sensors conflicts with strict resource constraints. This paper presents the algorithms for orientation calculation used on-board TU Berlin’s helicopter UAV MARVIN as an ex- ample of “low-resource” orientation measurement. While rotation rate sensors provide short-term data, acceleration and magnetic field sensors are fused for delayed on-line cal- ibration, using GPS to eliminate kinematic acceleration effects. All maths are performed using 32 bit fixed point arithmetics, involving certain “crude” simplifications. The algo- rithm’s performance,and computational complexity are compared,to a full Kalman Filter fusion of the sensors, justifying the use of the specialized and simplified approach.
Small unmanned air vehicles (UAVs) and micro air vehicles (MAVs) have payload and power constraints that prohibit heavy sensors and powerful processors. This paper presents real-time attitude and position estimation solutions that use small, inexpensive sensors and low-power microprocessors. In connection with an Extended Kalman Filter attitude estimation scheme, a novel method for dealing with latency in real-time is presented using a distributed-in-time architecture. Essential to small UAV or MAV missions is the ability to navigate precisely. To re-duce computational overhead and to simplify design, a cascaded filter approach to position estimation is used. The design is insensitive to noise and to loss of GPS lock. Simulation and hardware tests show that the algorithms operate in real-time and are suitable for control, stabilization, and navigation.
We are developing a system for autonomous navigation of unmanned aerial vehicles (UAVs) based on computer vision. A UAV is equipped with on-board cameras and each UAV is provided with noisy estimates of its own state, coming from GPS/INS. The mission of the UAV is low altitude navigation from an initial position to a final position in a partially known 3-D environment while avoiding obstacles and minimizing path length. We use a hierarchical approach to path planning. We distinguish between a global offline computation, based on a coarse known model of the environment and a local online computation, based on the information coming from the vision system. A UAV builds and updates a virtual 3-D model of the surrounding environment by processing image sequences and fusing them with sensor data. Based on such a model the UAV will plan a path from its current position to the terminal point. It will then follow such path, getting more data from the on-board cameras, and refining map and local path in real time.
Applying low-cost sensors for the Guidance, Navi- gation and Control (GNC) of an autonomous Uninhibited Aerial Vehicle (UAV) is an extremely challenging area. This paper presents the real-time results of applying a low-cost Inertial Measurement Unit (IMU) and Global Positioning System (GPS) receiver for the GNC. The INS/GPS navigation loop provides continuous and reliable navigation solutions to the guidance and flight control loop for autonomous flight. With additional air data and engine thrust data, the guidance loop computes the guidance demands to follow way-point scenarios. The flight control loop generates actuator signals for the control surfaces and thrust vector. The whole GNC algorithm was implemented within an embedded flight control computer. The real-time flight test results show that the vehicle can perform the autonomous flight reliably even under high maneuvering scenarios.
The performance of UAV is dependent greatly upon onboard sensors due to its characteristics of unmanned operated vehicle. The navigation sensor, which informs where UAV is flying, also is one of those onboard sensors. Small UAV needs the navigation system with the compact, light, cheap and precise navigation solution. As the inertial sensor for precise air navigation is very expensive, it is not popular in small aircraft and UAV. While GPS services a seamless navigation with cheap receiver, it may not receive the satellite signal by the obstacles or the signal jamming. It is GPS/INS sensor fusion that might overcome these constraints. GPS receiver on air vehicle may happen to lose the signal in a dynamic environment such as aircraft maneuver. The multiple GPS antennas were used to increase the coverage of GPS receiver. The ground test showed that GPS/INS sensor fusion system could provide well the attitude information as well as the trajectory according to a vehicle movement.
Hori-zon aided low-cost GPS/INS integration for autonomous micro air vehicle navigation, in: First European Micro Air Vehicle Conference and Flight Competition EMAV
- S Winkler
- H W Schulz
- M Buschmann
- T Kordes
- P Vörsmann
S. Winkler, H.W. Schulz, M. Buschmann, T. Kordes, P. Vörsmann, Hori-zon aided low-cost GPS/INS integration for autonomous micro air vehicle navigation, in: First European Micro Air Vehicle Conference and Flight Competition EMAV 2004, 13–14 July, Braunschweig, Germany, 2004.
Flight test results of GPS/INS navigation loop for an autonomous unmanned aerial vehicle (UAV), in: ION GPS
- J.-H Kim
- S Sukkarieh
J.-H. Kim, S. Sukkarieh, Flight test results of GPS/INS navigation loop
for an autonomous unmanned aerial vehicle (UAV), in: ION GPS 2002,
24-27 September, Portland, OR, USA, 2002, pp. 510-517.
Stabilizing a four-rotor helicopter using computer vision
- C Schlaile
- J Wendel
- G F Trommer
C. Schlaile, J. Wendel, G.F. Trommer, Stabilizing a four-rotor helicopter
using computer vision, in: First European Micro Air Vehicle Conference
and Flight Competition EMAV 2004, 13-14 July, Braunschweig, Germany, 2004.
Trommer, MAV attitude estimation using low-cost MEMS inertial sensors and GPS
- J Wendel
- O Meister
- R Mönikes
- C Schlaile
J. Wendel, O. Meister, R. Mönikes, C. Schlaile, G.F. Trommer, MAV
attitude estimation using low-cost MEMS inertial sensors and GPS, in:
Proceedings of the Institute of Navigation Annual Meeting 2005, Boston,
MA, USA, 2005.
Orientation sensing for he-licopter UAVs under strict resource constraints, in: First European Micro Air Vehicle Conference and Flight Competition EMAV
- M Musial
- C Deeg
- V Remuß
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