An aircraft and provide information about flight performance and local microclimate

Includes abstract.Includes bibliographical referencesThe application of using Unmanned Aerial Vehicles (UAVs) to locate thermal updraft currentsis a relatively new topic. It was first proposed in 1998 by John Wharington, and, subsequently, several researchers have developed algorithms to search and exploit thermals. However, few people have physically implemented a system and performed field testing. The aim of this project was to develop a low cost system to be carried on a glider to detect thermals effectively. A system was developed from the ground up and consisted of custom hardware and software that was developed specifically for aircraft. Data fusion was performed to estimate the attitude of the aircraft; this was done using a direction cosine (DCM) based method. Altitude and airspeed data were fused by estimating potential and kinetic energy respectively; thus determining the aircraft’s total energy. This data was then interpreted to locate thermal activity. The system comprised an Inertial Measurement Unit (IMU), airspeed sensor, barometric altitude sensor, Global Positioning System (GPS), temperature sensor, SD card and a realtime telemetry link. These features allowed the system to determine aircraft position, height, airspeed and air temperature in realtime. A custom-designed radio controlled (RC) glider was constructed from composite materials in addition to a second 3.6 m production glider that was used during flight testing. Sensor calibration was done using a wind tunnel with custom designed apparatus that allowed a complete wing with its pitot tube to be tested in one operation. Flight testing was conducted in the field at several different locations over the course of six months. A total of 25 recorded flights were made during this period. Both thermal soaring and ridge soaring were performed to test the system under varying weather conditions. A telemetry link was developed to transfer data in realtime from the aircraft to a custom ground station. The recorded results were post-processed using Matlab and showed that the system was able to detect thermal updrafts. The sensors used in the system were shown to provide acceptable performance once some calibration had been performed. Sensor noise proved to be problematic, and time was spent alleviating its effects

Fusion of sensor information to measure the total energy of an aircraft and provide information about flight performance and local microclimate

The application of using Unmanned Aerial Vehicles (UAVs) to locate thermal updraft currents is a relatively new topic. It was first proposed in 1998 by John Wharington, and, subsequently, several researchers have developed algorithms to search and exploit thermals. However, few people have physically implemented a system and performed field testing. The aim of this project was to develop a low cost system to be carried on a glider to detect thermals effectively. A system was developed from the ground up and consisted of custom hardware and software that was developed specifically for aircraft. Data fusion was performed to estimate the attitude of the aircraft; this was done using a direction cosine (DCM) based method. Altitude and airspeed data were fused by estimating potential and kinetic energy respectively; thus determining the aircraft's total energy. This data was then interpreted to locate thermal activity. The system comprised an Inertial Measurement Unit (IMU), airspeed sensor, barometric altitude sensor, Global Positioning System (GPS), temperature sensor, SD card and a realtime telemetry link. These features allowed the system to determine aircraft position, height, airspeed and air temperature in realtime. A custom-designed radio controlled (RC) glider was constructed from composite materials in addition to a second 3.6 m production glider that was used during flight testing. Sensor calibration was done using a wind tunnel with custom designed apparatus that allowed a complete wing with its pitot tube to be tested in one operation. Flight testing was conducted in the field at several different locations over the course of six months. A total of 25 recorded flights were made during this period. Both thermal soaring and ridge soaring were performed to test the system under varying weather conditions. A telemetry link was developed to transfer data in realtime from the aircraft to a custom ground station. The recorded results were post-processed using Matlab and showed that the system was able to detect thermal updrafts. The sensors used in the system were shown to provide acceptable performance once some calibration had been performed. Sensor noise proved to be problematic, and time was spent alleviating its effects. Results showed that the system was able to measure airspeed to within ± 1 km/h. The standard deviation of the altitude estimate was determined to be 0.94 m. This was deemed to be satisfactory. The system was highly reliable and no faults occurred during operation. In conclusion, the project showed that inexpensive sensors and low power microcontrollers could be used very effectively for the application of detecting thermals

Robust Multi-sensor Data Fusion for Practical Unmanned Surface Vehicles (USVs) Navigation

The development of practical Unmanned Surface Vehicles (USVs) are attracting increasing attention driven by their assorted military and commercial application potential. However, addressing the uncertainties presented in practical navigational sensor measurements of an USV in maritime environment remain the main challenge of the development. This research aims to develop a multi-sensor data fusion system to autonomously provide an USV reliable navigational information on its own positions and headings as well as to detect dynamic target ships in the surrounding environment in a holistic fashion. A multi-sensor data fusion algorithm based on Unscented Kalman Filter (UKF) has been developed to generate more accurate estimations of USV’s navigational data considering practical environmental disturbances. A novel covariance matching adaptive estimation algorithm has been proposed to deal with the issues caused by unknown and varying sensor noise in practice to improve system robustness. Certain measures have been designed to determine the system reliability numerically, to recover USV trajectory during short term sensor signal loss, and to autonomously detect and discard permanently malfunctioned sensors, and thereby enabling potential sensor faults tolerance. The performance of the algorithms have been assessed by carrying out theoretical simulations as well as using experimental data collected from a real-world USV projected collaborated with Plymouth University. To increase the degree of autonomy of USVs in perceiving surrounding environments, target detection and prediction algorithms using an Automatic Identification System (AIS) in conjunction with a marine radar have been proposed to provide full detections of multiple dynamic targets in a wider coverage range, remedying the narrow detection range and sensor uncertainties of the AIS. The detection algorithms have been validated in simulations using practical environments with water current effects. The performance of developed multi-senor data fusion system in providing reliable navigational data and perceiving surrounding environment for USV navigation have been comprehensively demonstrated

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

Cooperative localization for autonomous underwater vehicles

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2009Self-localization of an underwater vehicle is particularly challenging due to the absence of Global Positioning System (GPS) reception or features at known positions that could otherwise have been used for position computation. Thus Autonomous Underwater Vehicle (AUV) applications typically require the pre-deployment of a set of beacons. This thesis examines the scenario in which the members of a group of AUVs exchange navigation information with one another so as to improve their individual position estimates. We describe how the underwater environment poses unique challenges to vehicle navigation not encountered in other environments in which robots operate and how cooperation can improve the performance of self-localization. As intra-vehicle communication is crucial to cooperation, we also address the constraints of the communication channel and the effect that these constraints have on the design of cooperation strategies. The classical approaches to underwater self-localization of a single vehicle, as well as more recently developed techniques are presented. We then examine how methods used for cooperating land-vehicles can be transferred to the underwater domain. An algorithm for distributed self-localization, which is designed to take the specific characteristics of the environment into account, is proposed. We also address how correlated position estimates of cooperating vehicles can lead to overconfidence in individual position estimates. Finally, key to any successful cooperative navigation strategy is the incorporation of the relative positioning between vehicles. The performance of localization algorithms with different geometries is analyzed and a distributed algorithm for the dynamic positioning of vehicles, which serve as dedicated navigation beacons for a fleet of AUVs, is proposed.This work was funded by Office of Naval Research grants N00014-97-1-0202, N00014-05-1-0255, N00014-02-C-0210, N00014-07-1-1102 and the ASAP MURI program led by Naomi Leonard of Princeton University

Surveying a Floating Iceberg With the USV SEADRAGON

The calving, drifting, and melting of icebergs has local, regional, and global implications. Besides the impacts to local ecosystems due to changes in seawater salinity and temperature, the freshwater influx and transport can have significant regional effects related to the ocean circulation. The increased influx of freshwater ice due to increase calving from ice shelves and the destabilization of the continental ice sheet will affect sea levels globally. In addition, drifting icebergs pose threats to offshore operations because they could damage offshore installations, e.g., pipelines and subsea manifolds, and interrupt marine transportation. Iceberg drift and deterioration models have been developed to better predict climate change and protect offshore operations. Iceberg shape is one of the most critical parameters in these models, but it is challenging to obtain because of iceberg movement caused by winds, waves, and currents. In this paper, we present an algorithm for iceberg motion estimation and shape reconstruction based on in-situ point cloud measurements. The algorithm is developed based on point cloud matching strategies, policy-based optimization, and Kalman filtering. A down-sampling method is also integrated to reduce the processing time for possible real-time applications. The motion estimation algorithm is applied to a simulated data set and field measurements collected by an Unmanned Surface Vehicle (USV) on a free-floating, translating, and rotating, iceberg. In the field data, the above-water iceberg surface was measured with a scanning LIDAR, while the below-water portion (0–50 m) was profiled using a side-looking multi-beam sonar. When applying the motion estimation algorithm to these two independent point cloud measurements collected by the two sensing modalities, consistent iceberg motion estimates are obtained. The resulting motion estimates are then used to reconstruct the iceberg shape. During the field experiment, additional oceanographic measurements, such as temperature, ocean current, and wind, were collected simultaneously by the USV. We have observed water upwelling and a colder and fresher water plume at the sea surface downstream the iceberg. Combining the iceberg shape rendering and the surrounding environmental measurements, we estimated the iceberg melting parameters due to the sensible heat flux and surface wave erosion at different iceberg sections

Navigation and autonomy of soaring unmanned aerial vehicles

The use of Unmanned Aerial Vehicles (UAV) has exploded over the last decade with the constant need to reduce costs while maintaining capability. Despite the relentless development of electronics and battery technology there is a sustained need to reduce the size and weight of the on-board systems to free-up payload capacity. One method of reducing the energy storage requirement of UAVs is to utilise naturally occurring sources of energy found in the atmosphere. This thesis explores the use of static and semi-dynamic soaring to extract energy from naturally occurring shallow layer cumulus convection to improve range, endurance and average speed. A simulation model of an X-Models XCalibur electric motor-glider is used in combination with a refined 4D parametric atmospheric model to simulate soaring flight. The parametric atmospheric model builds on previous successful models with refinements to more accurately describe the weather in northern Europe. The implementation of the variation of the MacCready setting is discussed. Methods for generating efficient trajectories are evaluated and recommendations are made regarding implementation. For micro to small UAVs to be able to track the desired trajectories a highly accurate Attitude Heading Reference System (AHRS) is needed. Detailed analysis of the practical implementation of advanced attitude determination is used to enable optimal execution of the trajectories generated. The new attitude determination methods are compared to existing Kalman and complimentary type filters. Analysis shows the methods developed are capable of providing accurate attitude determination with extremely low computational requirements, even during extreme manoeuvring. The new AHRS techniques reduce the need for powerful on-board microprocessors. This new AHRS technique is used as a foundation to develop a robust navigation filter capable of providing improved drift performance, over traditional filters, in the temporary absence of global navigation satellite information. All these algorithms have been verified by flight tests using a mixture of manned and unmanned aerial vehicles and avionics developed specifically for this thesis

Collaborative control of wave glider platforms - Local Communication and Sea State Estimation

Climate change is the focus of many oceanography and marine engineering researchers, with possible links between climate change and the carbon cycle in the Southern Ocean being considered. This type of investigation requires modern and cost-effective tools to conduct surveys and collect data from the ocean. The self-propelled unmanned surface vessel, the Liquid Robotics Wave Glider, was designed primarily as a marine research tool and offers several advantages over existing research vessels and other tools employed for data acquisition in the ocean. The main advantages are its robustness at sea, i.e. its ability to withstand extreme weather conditions, its propulsion energy source, which is the wave energy, and its customisable electronics payload. The inter-platform communication strategy of the Wave Glider inspired a few engineering questions, one of which is the focal point of this research: whether Low Power Wide Area Network (LPWAN) technology can be used to set up a local communication system enabling the collaboration of two or more Wave Gliders and reduce the cost, in terms of power and communication channels, involved in the communication with the Wave Glider platforms during missions. This research considers various LPWAN technologies available on the market and proposes LoRaWAN technology for the local communication system. LoRaWAN was selected as it presented a robust radio modulation and had growing support in the industry. In this research, a LoRa-based network of two nodes was developed, implemented and tested over the surface of the ocean. It was found that the system performs well over a distance of 1 km with both antennas having one end at the mean surface level of the sea. With the intention to increase the range of the platform and achieve a reliable and robust system, the research continued with the study of the influence of the surface waves on the proposed local communication system by exploring, firstly, the impact of seawater and, secondly, the wave height on signal transmission. The first study investigated the influence that the electromagnetic properties of seawater may have on the transmission of signals from one node to the second through simulations using the computational electromagnetic package FEKO. It revealed that, at the frequency of operation, which was 868 MHz, seawater reacted as a lossy conductor and reflected the signal upward, with negligible power penetrating the surface of the ocean. The subsequent study reviewed the statistical properties of the ocean surface waves in a sea of deep waters and proposed a relationship between the wind speed (or surface wave elevation), the antenna height, the distance separation between the two nodes and the probability of the presence of a line of sight (LoS) between the two nodes. This relationship quantifies the expected result that the probability of the LoS diminishes as the wind speed or the distance between the two nodes increases, whereas it improves with an increase in the antenna height. The last part of the research focused on initial works on sea state estimation using the lossless wave equation and Kalman Filter to provide 3D sea surface elevations that would be used to change to the probability of the LoS calculated previously in the research. Indeed, using the local communication to share the point-wise sea state data can be exploited to estimate the sea state over a rectangular region delimited to include these points. Sea state estimation is expected to enhance the joint navigation and coordination of the platforms and consequently, boost the probability of the LoS through the transmission at the crest of the waves. During the development of the Kalman Filter model, it was discovered that the sample time and the sample space significantly affect the performance and the stability of the discretised models. However, a carefully selected sampling time and sample space exhibited a stable system model. The results of the Kalman filtering were a realistic sea state estimate with a minimum error at the locations in the surrounding of the measurements