Development of autonomous unmanned helicopter based agricultural remote sensing system

Purpose – A simplified global positioning system (GPS) error model including models for a variety of abnormal operational conditions and failures is developed to provide simulation tools for the design, testing, and evaluation of autonomous flight fault tolerant control laws. The paper aims to discuss these issues. Design/methodology/approach – Analysis and experimental data are used to build simplified models for GPS position and velocity errors on all three channels. The GPS model is interfaced with West Virginia University unmanned aerial vehicles (UAV) simulation environment and its utility demonstrated through simulation for several autonomous flight scenarios including GPS abnormal operation. Findings – The proposed simplified GPS model achieves desirable levels of accuracy and realism for all components for the purpose of general UAV dynamic simulation and development of fault tolerant autonomous flight control laws. Research limitations/implications – The simplified GPS model allows investigating GPS malfunction effects on the performance of autonomous UAVs and designing trajectory tracking algorithms with advanced fault tolerant capabilities. Practical implications – The simplified GPS model has proved to be a flexible and useful tool for UAV simulation and design of autonomous flight control laws at normal and abnormal conditions. Originality/value – The outcomes of this research effort achieve a level of detail never attempted before in modeling GPS operation at normal and abnormal conditions for UAV simulation and autonomous flight control laws design using a simplified framework.

In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed for low-cost navigation of ground vehicles. The ANFIS structure is proposed for fusing the measurements of the Global Positioning System (GPS) and inertial measurement unit (IMU). Low-cost GPS receivers provide the position and velocity of the vehicle at a lower rate than the IMU sampled vehicle dynamics. Additionally, the GPS measurements might be missed/delayed due to receiver's inability to lock on the signal or due to obstruction from neighboring vehicle's or infrastructures. Therefore, the variable delay between the IMU and GPS signals is selected as an additional input to the ANFIS architecture. The performance of the proposed ANFIS architecture is demonstrated through experimental results. The instrumentation system used in this experiment consists of a microcontroller system, a GPS receiver, and an IMU.

Unmanned Aircraft Systems (UASs) can collect high resolution and high quality images for local mapping. Before the UAS images can be used for accurate mapping tasks in local areas, the precise position and orientation of the UAS images should first be determined. Direct georeferencing by POS (Position and Orientation System), a combination of GPS (Global Positioning System) and IMU (Inertial Measurement Unit), is the best choice; however, most commercial UASs cannot carry highly accurate IMUs because of the limited payload. Therefore, this study will discuss the accuracy of indirect georeferencing for UAS images. One approach for indirect georeferencing is general aerial triangulation (AT) by using well-distributed ground control points (GCPs). The other one is GPS-supported AT with GPS observations as airborne controls. In this paper, the camera is calibrated by the field method, and the accuracy of these two approaches for indirect georeferencing is presented. Based on 20 horizontal check points and 29 vertical check points, this study shows the stereoscopic viewing accuracy of general AT for UAS images, collected by Canon EOS 5D Mark II camera with 24 mm F/1.4L II USM lens at a flying height of 550 m, is about 0.26 m (ca. 1.73 pixels) in planimetry and 0.27 m (ca. 1.80 pixels) in height. GPS-supported AT produced the stereoscopic viewing accuracy about 0.44 m (ca. 2.93 pixels) in planimetry and 0.55 m (ca. 3.67 pixels) in height. The test results show that the accuracy of these two indirect georeferencing approaches of fixed-wing UAS images can be used for updating local 1/5,000 topographic maps in Taiwan.

This paper presents an adaptive learning method for data fusion in autonomous\ndriving vehicles. The localization is based on the integration of Inertial\nMeasurement Unit (IMU) with two Real-Time Kinematic (RTK) Global Positioning\nSystem (GPS) units in an adaptive Kalman filter (KF). The observability\nanalysis reveals that $i$) integration of a single GPS with IMU does not\nconstitute an observable system; $ii$) integration of two GPS units with IMU\nresults in a locally observable system provided that the line connecting two\nGPS antennas is not parallel with the vector of the measured acceleration,\ni.e., the sum of inertial and gravitational accelerations. The latter case\nmakes it possible to compensate for the error in the estimated orientation due\nto gyro drift and its bias without needing additional instruments for absolute\norientation measurements, e.g., a magnetic compass. Moreover, in order to cope\nwith the fact that GPS systems sometimes lose their signal and receive\ninaccurate position data, the self-tuning filter estimates the covariance\nmatrix associated with the GPS measurement noise. This allows the KF to\nincorporate GPS measurements in the data fusion process heavily only when the\ninformation received by GPS becomes reliably available.\n

In this paper, we describe the design and performance of a prototype multi-rotor unmanned aerial vehicle (UAV) platform featuring autonomous flying based on an inertial measurement unit (IMU) for use in bluetooth communication environments. Although there has been a fair amount of study on free-flying UAVs with multiple rotors, the more recent trend has been to outfit hexarotor UAVs with gimbals to support various services. This paper introduces the hardware and software systems for very compact and autonomous hexarotors, which can be used for search & rescue and surveillance missions without external assistance systems like ground station computers, high-performance remote control devices, or vision systems. This study included the construction of the test hexarotor platform, the implementation of an IMU, mathematical modeling, and simulation of the UAV. Furthermore, the hexarotor UAV with the implemented IMU was connected to a microcontroller unit (ARM-cortex) board. The microcontroller was able to control the rotational speed of the rotors and used the IMU measurements as input signals. A control simulation and experiment with the real system were implemented for the test platform, and the results were evaluated and compared.

A trajectory reference system based on the Global Positioning System (GPS) can be used instead of conventional radars during missile flight tests. The high quality of the GPS-based trajectory reference makes it useful for evaluating the performance of the missiles' Inertial Measurement Units (IMUs). Such a system was installed and flight tested on two recently launched ballistic missiles. The GPS hardware configuration used on these flights is described. A Kalman filter approach is used to estimate individual IMU errors based on the GPS range and delta range data. The ability of the GPS-based system to identify IMU errors is compared to that of radar; GPS is found to provide superior estimates of the IMU errors.

Rapid delineation of potential damage from hurricanes to the natural and human-modified landscape is critical for disaster management and for assessment of ecological and economic impacts.Geospatial technologies including Remote Sensing (RS), Geographic Information Systems/Science (GIS), and Global Positioning Systems (GPS) provide new tools for delineating damage potential and for assessing destruction after natural disasters.Historically, airborne and space RS missions have been used for post-hurricane impact assessment, but these data are limited in a variety of ways.Space-based RS data have limitations including differing specifications for spatial, spectral, and temporal resolution.As an example, predicting damage or assessing damage from land-falling hurricanes via satellite data is often hampered by temporal resolution (re-visit frequency) that can result in unavailability of cloudfree images over impacted areas.Airborne RS data are not temporally limited to a specific window of ground coverage and can even acquire data during cloudy conditions.Airborne RS instruments may provide data of higher spectral and spatial resolution than space-borne instruments; however complete 'wall-to-wall' coverage of landscapes impacted by tropical systems as large as hurricane Katrina can be impractical and prohibitively expensive.Consequently, many geospatial analysts first use space-borne data and geospatial modeling at a relatively coarse-resolution to provide an overview of hurricane impacted areas, and then they use this information to pinpoint locations where they need to acquire airborne RS data for higher-resolution subsets of damaged areas.Therefore, there is a need to develop more accurate geospatial models that incorporate readily available data at a regional scale to assist emergency and disaster management agencies with their decision-support.According to Hodgson et al., (2010), agencies with first responder responsibilities have expressed the critical need for image acquisitions over disaster impacted areas within 24 hours, or within three days of the event at a minimum.Given the limitations for rapid acquisition of RS imagery over hurricane-impacted areas, researchers at Mississippi State University in coordination with Mississippi Emergency Management Agency (MEMA) advisors have developed a geospatial modeling-based tool that enables rapid depiction (within 24 -48 hours after landfall) of a damage probability statistical grid which incorporates readily available data sources such as storm surge predictions and precipitation and wind forecasts.This grid can be overlain on a variety of image and GIS base layers to help plan for evacuation procedures prior to landfall and to help orient and manage the effective allocation of resources to impacted areas.www.intechopen.

Information communication technology related vehicle services need to support location and the transmission of communication and traffic information between vehicles, or between vehicles and infrastructure. In particular, the technology for the measurement of the accurate location of a vehicle is dependent on location-determination technology like Global Positioning System, and this technology is very important for vehicle driving and location services. If, however, a vehicle is in a Global Positioning System radio-shadow area, neither a Global Positioning System nor a Differential Global Positioning System can accurately measure the corresponding location because of a high error rate caused by the shadowing intervention. Even an Inertial Measurement Unit could provide inaccurate location data due to sensor drift faults around corners and traffic-road speed dumps. Vehicles, therefore, need an absolute location to prevent the provision of inaccurate vehicle-location data that is due to radio-shadow areas and relational Inertial Measurement Unit positions. To achieve this, we assume that vehicle-to-infrastructure communication is possible between a vehicle and roadside unit in Vehicular Ad hoc Networks. We used iBeacon at the roadside unit and revised its Universally Unique Identifier so that it generates absolute Global Positioning System location data; that is, moving vehicles can receive absolute Global Positioning System data from the roadside unit-based iBeacon. We compared the proposed method with current Global Positioning System and Inertial Measurement Unit systems for the following two cases: one with a radio-shadow area and one without. We proved that the proposed method generates location data that are more accurate than those of the other methods.

Global Positioning System (GPS) spoofing is a common mean to cheat an Unmanned Aerial Vehicle (UAV) which is guided by the INS/GPS integrated navigation system. Detecting the spurious GPS signal is the precondition of anti-spoofing for UAVs. A detection system of GPS spoofing is designed based on dynamics identification. The centroid motion model and attitude motion model were established. The measurement information from inertial measurement units (IMUs) and positioning information from GPS both are visualized responses to the dynamic laws of UAVs. These two types of measurement information were separately used to estimate the dynamics parameters of UAVs online using two Extended Kalman Filters (EKFs). The detection of spurious GPS signals was realized effectively based on the monitoring of relative estimation errors. Numerical simulations demonstrated that the dynamics parameters could be estimated online by the combined filters. And the precision and accuracy of filter #1, which was based on IMU information, were both higher than those of filter #2, which was based on GPS information. When the spurious GPS signal was just zero-order continuous with the true signal at the penetration point, a pulse relative error would occur at the penetration point; when the spurious GPS signal was first-order continuous with the true signal at the penetration point, a step relative error would occur. Furthermore, relative systematic errors matched with the period of spurious signals.

The book has been divided into six chapters. The first chapter “Introduction to remote sensing” in this chapter we have given basics knowledge of remote sensing, it include the definition, history of the satellite systems, electromagnetic spectrum and resolutions etc. The second chapter of “Aerial Remote Sensing” deals with elements, interpretation, types of photo and measurements. The third chapter represents the “Satellite remote sensing”. The fourth chapter analysed “Global Positioning System”, it include the GPS procedure, types and developments. The fifth chapter is “Introduction to Geographical Information System” and covered basics information about GIS. The sixth chapter included applications of remote sensing, geographical information system and Global positioning systems. This book is very useful to bachelor, post graduates’ degree of geography and research scholars can get the some ideas and knowledge about the Geospatial Technologies. This book is prepared with very basic and common information. Geographic knowledge of these technologies will provide students with the skills needed to meet the demands of their diverse careers in the global workplace.

Land vehicles rely mainly on global positioning system (GPS) to provide its position with consistent accuracy. However, GPS receivers may encounter frequent GPS outages within urban areas where satellite signals are blocked. In order to overcome this problem, GPS is usually combined with inertial sensors (accelerometers and gyroscopes) mounted inside the vehicle to obtain a reliable navigation solution, especially during GPS outages. Neuro ? Fuzzy modules were used to fuse data from GPS and inertial sensors in real-time. This research optimizes the operation of these modules by implementing a temporal window-based cross-validation approach during the update procedure. A field test data was used to examine the performance of the proposed method and the results discuss the merits and the limitations of the proposed technique.

Position determination using satellite navigation system has grown significantly. It provides geospatial with global coverage called GNSS (Global Navigation System Satellite). GNSS satellites consists of GLONASS, GPS (Global positioning system) and Galileo.GPS is the most commonly used system and it is known to its capability to determine 3D position on the surface of the earth. In order to determine the position, a GPS receiver must be able to receive signals from at least four GPS satellites. However, the determination of position in condensed areas such as tunnels, area surrounded by high rise buildings, highly forested and in other closely-knit areas is not achieved because satellite signals cannot reach the receiver in the above-mentioned areas and also others where the signals are reflected before being received by a GPS receiver. In this paper, we present the algorithm to fuse GPS and the inertial measurement unit (IMU) to enable positioning in the above-mentioned Condensed Areas. The standard deviations of the two measurements show that GPS-IMU is better than GPS alone, the standard deviation when satellite outages occurred is - 4.57475 for GPS-IMU measurements and 0.218675 for GPS observations. We presented the results in graphics and it shows that GPS measurements are easily disturbed by external influence such as multipath but GPS-IMU graphic is continuous and robust. The advantages and disadvantages of GPS and INS are complementary and make them work together to enable the accurate measurements in the areas mentioned above. Integration of INS and GNSS can be classified into three types, loosely coupled Kalman filter, tightly coupled Kalman filters and ultratight coupled Kalman filter. In this research we used loosely coupled Kalman filter and tightly coupled Kalman filters to combine GPS and INS in one system.

Precision agriculture, site-specific application of inputs tailored to the needs of the crop, is one of the new ways that modern agriculture could potentially maintain or enhance crop yields and minimize environmental pollution. Knowledge about variations in vegetation species and community distribution patterns, alterations in vegetation phenological cycles, and modifications in the plant physiology and morphology provide valuable insight into the climatic, edaphic, geologic, and geophysical characteristics of Earth’s areas (Janetos & Justice, 2000). During the past decade remote sensing techniques have been widely used to monitor crops throughout their growing period to help in making decisions for good agricultural practices. Spectral remote sensing methods provide the possibility for early, efficient, objective, and non-destructive evaluation of plant responses to different stress factors of the environment (Campbell et al., 2007; Govender et al., 2009; Li et al., 2010). Field remote sensing applications addressed agriculture and forestry survey, fire detection and fire-fuel mapping, mineral mapping, and atmospheric modelling. Airborne, space-borne and hand-held technologies are commonly used to investigate the spectral responses of plants. Hyperspectral remote sensing makes possible to enhance significantly the spectral measurement capabilities over conventional remote sensing sensor systems, as well as to improve the spectral information content. This entails detailed assessment of the changes in the physiological stage of plants in response to the changes in the environment (ZarcoTejada et al., 2002; Steele et al., 2008a), detecting of early-stage vegetation stress (Krezhova et al., 2005; Ouyang et al., 2007), discriminating land cover types (Flamenco-Sandoval et al., 2007), leaf pigment concentrations (Coops et al., 2003), modelling quantitative biophysical and yield characteristics of agricultural crops (Delalieux et al., 2009a; Chatzistathisa et al., 2011). Ground-truth is essential for detecting plant stress, and two commonly used ground-based optical methods, leaf spectral reflectance and chlorophyll fluorescence, are reviewed for their usefulness and practical application. When these methods were combined with remarkable advances in Global Positioning System (GPS) receivers, geographic information systems (GIS), and enhanced crop simulation models, remote sensing technology has the potential to transform the ways that growers manage their lands and implement precision farming techniques (Upchurch, 2003; Hatfield, et al., 2008; Shuanggen & Komjathy, 2010). To obtain accurate and complementary comparative assessments for plant responses to the environmental changes, methods have been applied from different research fields remote

The recent increased pressure on slope land development in Taiwan has changed the land use pattern in many reservoir watershed areas and affected the hydrologic cycle. There were many local researches examined this related issue, but none of that can fully describe the relationship between water conservation capacity and land use patterns in watershed area. This study is to integrate and analyze the impact to water conservation from deforestation or other land use pattern changes. In this study, an integrated methodology combining Geographic Information Systems (GIS), Remote Sensing (RS), Global Positioning System (GPS), and hydrologic simulation program (HSPF) is utilized to define the factor-consequence relationship of this issue from digital database, satellite images, land use pattern identification, and hydrology model, in respectively. The first part of this research was to determine those factors influenced the hydrological processes, which includes water balance parameters extraction for various input data in hydrological model. Secondly, an environmentally sound database was built using GIS, RS and GPS technologies. Then HSPF hydrologic model was used to estimate the quantity of water conservation capacity. The last part was to link the database with water conservation model and estimate the changes due to land use development. This research also use ArcView Avenue Script and C++ to establish an user interface, that provides users to input data and output the results. It can get the volume change of water conservation capacity caused by land use change real time. It provides watershed manager the reference in reservoirs watershed area conservation and development policy.

The use of deep neural networks for semantic segmentation of RS (remote sensing) images is an important research topic in the field of remote sensing intelligent interpretation, which has significant application value in urban planning, disaster assessment, carbon estimation, and other fields. However, high-resolution RS images bring problems of large data scale, high computational complexity, diverse scales, and irregular shapes. Moreover, there may be distant spatial relationships between target objects, requiring the utilization of long-range contextual information for accurate semantic segmentation. Inspired by the powerful global modelling capability of the Swin Transformer, we propose a new RS image semantic segmentation network. Swin Transformer is used as an encoder to extract global features. To prevent the loss of local details in the encoding stage, one LKA-PM (LKA-Patch Merging) module is designed to extract local detail information by decomposing large kernel convolutions, thereby enhancing the feature representation ability of small objects. Furthermore, to decode global and local features and obtain multi-scale information, one WORC (Weighted Operation Re-parameterization Convolution) module is designed to improve the adaptability and accuracy of the model to different RS images. Finally, to preserve spatial details during decoding and reduce the problem of boundary blurring caused by occlusion in RS imagery, the Coordswin module is designed to provide coordinate information and improve the discrimination ability of similar objects. Experimental results demonstrate that our CW-SwinUNet achieves excellent segmentation performance (91.8 % F1 and 85.2 % MIoU) on the Vaihingen dataset and (93.8 % F1 and 87.9 % MIoU) on Potsdam datasets, surpassing other state-of-the-art methods. The source code of the proposed CW-SwinUNet is available at https://github.com/xmy1135/cwswin/tree/main.