Download or read book Terrain Relative Navigation for Sensor limited Systems with Application to Underwater Vehicles written by Deborah Kathleen Meduna and published by Stanford University. This book was released on 2011 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: Terrain Relative Navigation (TRN) provides bounded-error localization relative to an environment by matching range measurements of local terrain against an a priori map. The environment-relative and onboard sensing characteristics of TRN make it a powerful tool for return-to-site missions in GPS-denied environments, with potential applications ranging from underwater and space robotic exploration to pedestrian indoor navigation. For many of these applications, available sensors may be limited by mission power/weight constraints, cost restrictions, and environmental effects (e.g. inability to use a magnetic compass in space). Such limitations not only degrade the accuracy of traditional navigation systems, but further impact the ability to successfully employ TRN. Consequently, despite numerous advances in TRN technology over the past several decades, the application of TRN has been restricted to systems with highly accurate and information-rich sensor systems. In addition, a limited understanding of the effects of map quality and sensor quality on TRN performance has overly restricted the types of missions for which TRN has been considered a viable navigation solution. This thesis develops two new capabilities for TRN methods, resulting in significantly increased TRN applicability. First, a tightly-coupled filtering framework is developed which enables the successful use of TRN on vehicles with both low-accuracy navigation sensors and simple, low-information range sensors. This new filtering framework has similarities to tightly-coupled integration methods for GPS-aided navigation systems. Second, a set of analysis and design tools based on the Posterior Cramer-Rao Lower Bound are developed which allow for reliable TRN performance predictions as a function of both sensor and map quality. These analyses include the development of a new terrain map error model based on the variogram which allows for performance prediction as a function of map resolution. These developed capabilities are validated through field demonstrations on Autonomous Underwater Vehicles (AUVs) operated out of the Monterey Bay Aquarium Research Institute (MBARI), where available sensing has been limited primarily by cost. These trials include a real-time, closed-loop demonstration of the developed tightly-coupled TRN framework, enabling 5m accuracy return-to-site on a sensor-limited AUV where traditional TRN methods failed to provide better than 150m accuracy. The results further demonstrate the accurate prediction capability of the developed performance bounds on fielded systems, verifying their utility as design and planning tools for future TRN missions.
Download or read book Terrain relative Navigation for Autonomous Underwater Vehicles written by Diane Eugenia Di Massa and published by . This book was released on 1997 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Navigation is a key technology for autonomous underwater vehicles (AUVs), and currently, it limits potential and existing vehicle capabilities and applications. This thesis presents a terrain-relative navigation system for AUVs that does not require the deployment of acoustic beacons or other navigational aids, but instead depends on a supplied digital bathymetric map and the ability of the vehicle to image the seafloor. At each time step, a bathymetric profile is measured and compared to a local region of the supplied map using a mean absolute difference criterion. The region size is determined by the current navigation uncertainty. For large regions, a coarse-to-fine algorithm with a modified beam search is used to intelligently search for good matches while reducing the computational requirements. A validation gate is defined around the position estimate using the navigation uncertainty, which is explicitly represented through a covariance matrix. A probabilistic data association filter with amplitude information (PDAFAI), grounded in the Kalman Filter framework, probabilistically weights each good match that lies within the validation gate. Weights are a function of both the match quality and the size of the innovation. Navigation updates are then a function of the predicted position, the gate size, all matches within the gate, and the uncertainties on both the prediction and the matches. The system was tested in simulation on several terrain types using a deep-ocean bathymetry map of the western flank of the Mid-Atlantic Ridge between the Kane and Atlantis Transforms. Results show more accurate navigation in the areas with greater bathymetric variability and less accurate navigation in flatter areas with more gentle terrain contours. In most places, the uncertainties assigned to the navigation positions reflect the ability of the system to follow the true track. In no case did the navigation diverge from the true track beyond the point of recovery.
Download or read book Terrain Relative Navigation for Sensor limited Systems with Application to Underwater Vehicles written by Deborah Kathleen Meduna and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Terrain Relative Navigation (TRN) provides bounded-error localization relative to an environment by matching range measurements of local terrain against an a priori map. The environment-relative and onboard sensing characteristics of TRN make it a powerful tool for return-to-site missions in GPS-denied environments, with potential applications ranging from underwater and space robotic exploration to pedestrian indoor navigation. For many of these applications, available sensors may be limited by mission power/weight constraints, cost restrictions, and environmental effects (e.g. inability to use a magnetic compass in space). Such limitations not only degrade the accuracy of traditional navigation systems, but further impact the ability to successfully employ TRN. Consequently, despite numerous advances in TRN technology over the past several decades, the application of TRN has been restricted to systems with highly accurate and information-rich sensor systems. In addition, a limited understanding of the effects of map quality and sensor quality on TRN performance has overly restricted the types of missions for which TRN has been considered a viable navigation solution. This thesis develops two new capabilities for TRN methods, resulting in significantly increased TRN applicability. First, a tightly-coupled filtering framework is developed which enables the successful use of TRN on vehicles with both low-accuracy navigation sensors and simple, low-information range sensors. This new filtering framework has similarities to tightly-coupled integration methods for GPS-aided navigation systems. Second, a set of analysis and design tools based on the Posterior Cramer-Rao Lower Bound are developed which allow for reliable TRN performance predictions as a function of both sensor and map quality. These analyses include the development of a new terrain map error model based on the variogram which allows for performance prediction as a function of map resolution. These developed capabilities are validated through field demonstrations on Autonomous Underwater Vehicles (AUVs) operated out of the Monterey Bay Aquarium Research Institute (MBARI), where available sensing has been limited primarily by cost. These trials include a real-time, closed-loop demonstration of the developed tightly-coupled TRN framework, enabling 5m accuracy return-to-site on a sensor-limited AUV where traditional TRN methods failed to provide better than 150m accuracy. The results further demonstrate the accurate prediction capability of the developed performance bounds on fielded systems, verifying their utility as design and planning tools for future TRN missions.
Download or read book Cooperative Terrain relative Navigation written by Adam Tadeusz Wiktor and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis introduces a new method to improve localization performance for teams of vehicles navigating cooperatively. When fusing measurements between multiple vehicles, the structure of the cooperative navigation network inherently introduces correlation between them, causing many traditional filter architectures to overconverge and become inconsistent. The algorithm presented in this thesis addresses this correlation and properly fuses measurements, allowing improved performance over other existing methods while still guaranteeing consistency. When restricted to linear, Gaussian systems, the covariance recovers 99% of the performance of an ideal centralized filter in some tests. Additionally, a proof is presented to guarantee that the algorithm is consistent under standard Kalman filter assumptions. The algorithm is also extended to apply to nonlinear systems, losing the guarantees of consistency (as with all Kalman filters) but achieving good performance in practice. This allowed the method to be tested in a laboratory experiment with real-world sensors. Finally, this thesis further extends the algorithm to apply to non-parametric particle filters, allowing for full cooperative Terrain-Relative Navigation (TRN) with multi-modal position estimates. This is demonstrated in simulation, where cooperative TRN is shown to provide a 63% reduction in localization error over standard single-vehicle TRN for one example mission, reducing the average error from 23.7m to 8.7m for a vehicle over flat terrain. The cooperative TRN algorithm is also demonstrated using field data from a team of Long-Range Autonomous Underwater Vehicles in Monterey Bay. In offline testing, the cooperative TRN method was able to correctly find the position of a vehicle when its own individual TRN filter was unable to converge. This demonstrates that the cooperative TRN algorithm is effective with real-world robotic systems, increasing localization accuracy and enabling new missions involving navigation in flat, unmapped, or changed terrain.
Download or read book Development and Testing of Navigation Algorithms for Autonomous Underwater Vehicles written by Francesco Fanelli and published by Springer. This book was released on 2019-04-16 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on pose estimation algorithms for Autonomous Underwater Vehicles (AUVs). After introducing readers to the state of the art, it describes a joint endeavor involving attitude and position estimation, and details the development of a nonlinear attitude observer that employs inertial and magnetic field data and is suitable for underwater use. In turn, it shows how the estimated attitude constitutes an essential type of input for UKF-based position estimators that combine position, depth, and velocity measurements. The book discusses the possibility of including real-time estimates of sea currents in the developed estimators, and highlights simulations that combine real-world navigation data and experimental test campaigns to evaluate the performance of the resulting solutions. In addition to proposing novel algorithms for estimating the attitudes and positions of AUVs using low-cost sensors and taking into account magnetic disturbances and ocean currents, the book provides readers with extensive information and a source of inspiration for the further development and testing of navigation algorithms for AUVs.
Download or read book Robust Adaptive Terrain relative Navigation written by Shandor Dektor and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Terrain-Relative Navigation (TRN) is an emerging technique for localization in natural environments. TRN augments a dead-reckoned solution with position fixes based on correlations with pre-stored maps. TRN is a particularly valuable tool for enabling missions for robots in regions without GPS, a category that includes the underwater environment as well as missions on other bodies in the solar system. The algorithms underlying TRN, however, have known issues with overconfidence in uninformative (e.g. flat) terrain. Overconfident estimates, also known as false peaks, are a significant problem as they can result in dangerous trajectories and mission failure. Making TRN robust to uninformative terrain is the focus of the work presented in this thesis. The interplay between map error, terrain correlation, and TRN filter overconfidence is the first focus of this thesis. TRN correlation techniques are shown to include, either implicitly or explicitly, a probabilistic model of terrain correlation, and that the most common method of TRN weighting implicitly models the terrain as uncorrelated. The degree of auto-correlation present in the terrain is related to the amount of variation in the terrain: greater variation in terrain height corresponds to lower correlation in the terrain and vice-versa. The uncorrelated terrain assumption is then demonstrated to be a source of false peaks. In informative terrain, where the variation in the terrain is large with respect to error in the map, the standard calculation produces reasonable results: peaks at the correct location. In uninformative terrain, when the variation in the terrain is small with respect to map error, standard correlation breaks down and is shown to produce overconfidence in the filter. Techniques are then developed for mitigating false peaks in uninformative terrain. The first technique developed in this thesis focuses on explicitly accounting for terrain correlation by using correlated Gaussian terrain models; while these methods have success in simulation, the computational cost of explicitly modeling terrain correlation makes them impractical for field applications. An alternate approach, exponentially down-weighting the standard weighting to account for the impact of the uncorrelated terrain assumption, is then proposed as a computationally tractable means of accounting for unmodeled terrain correlation. The exponential down-weighting technique is termed the adaptive TRN filter. It follows on work from the statistics community designed to improve the robustness of probabilities computed using incorrect models, and achieves this robustness by matching a bound on the likelihood of false peaks. The ``robust adjusted likelihood'' approach is adapted to the TRN likelihood function and used to develop the relation between terrain correlation and the necessary degree of down-weighting. The adaptive technique is further developed for field work using real-time TRN filters. The adaptive TRN filter is validated using two platforms: an Autonomous Underwater Vehicle (AUV) and an ATRV-Jr ground rover. The AUV TRN filter is developed for an AUV correlating with range measurements of the terrain. The ground rover filter is developed for operations on the Moon or Mars, where direct measurements of altitude are unavailable, and the TRN filter must therefore correlate on gradient. As most maps are elevation based and must be differentiated to produce a gradient map, the map noise is increased and makes accounting for map error critical in this case. The effectiveness of the adaptive TRN filter is demonstrated using field data from MBARI AUV runs over flat terrain in Monterey Bay, and on ATRV-Jr field data taken at the Stanford campus. Both cases demonstrate meter-level performance when operating in informative terrain, and effective mitigation of false convergence over uninformative terrain when compared to filter performance using the unadjusted weighting.
Download or read book Waypoint Generation and Route Planning for Terrain Relative Navigation written by Steven Patrick Krukowski and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Navigation is a key capability for autonomous systems, requiring both determination of the system's position relative to the target and development of a plan to get there. In some applications, the amount of information incorporated into the position estimate depends on the location of the autonomous system. When the information is sparse, the route to the target site can impact the system's ability to localize. Planning a route to increase localization accuracy can greatly increase the likelihood of successfully navigating to the site. Many robotic systems rely on GPS for localization; however, this option is unavailable underwater. Terrain Relative Navigation (TRN) is a localization technique that was previously developed to enable Autonomous Underwater Vehicles (AUVs) to perform missions to sites on the seafloor. Accurate localization requires observation of varying, informative terrain. Many underwater missions are over flat terrain with only sparse areas of high information. A well planned route that guides the AUV over the informative terrain can increase localization accuracy when arriving at the target. Route planning for TRN presents several challenges that are not addressed by current planning techniques. Belief space motion planning techniques have been applied to similar problems; however, these techniques rely on the use of a linearized Gaussian position estimate. This assumption is not valid for TRN. TRN uses a particle filter, a Monte Carlo localization technique, to handle the multi-modal distribution that may arise from the information sparsity. The planning techniques for linearized Gaussian estimates extend the state space with a compact representation of the positional uncertainty and solve for a path using traditional search methods. These methods are not easily adaptable to use with a particle filter. Several planning techniques for partially observable Markov decision processes (POMDPs) are adapted for particle filter estimation; however, POMDP solution techniques solve for an approximate optimal closed-loop policy. Since the AUV routes are determined prior to starting the mission, the policy search techniques for particle filters do not apply for this application. This thesis presents a planning technique called the TRN Route Planning Method (TRN-RPM), which generates the intermediate waypoints of the route. TRN-RPM formulates route planning as an optimization to maximize the probability of arriving within a given threshold from a target site. A simulation of the AUV was developed to generate Monte Carlo samples of the target error distribution from different vehicle trajectories along a given route. These samples are used to approximate a route's success probability. Monte Carlo estimation greatly increases the computation required. Therefore a two-step method was developed to limit the time needed for route selection. First, the number of potential waypoint locations is limited by using a heuristic based on the posterior Cramer--Rao lower bound to place waypoints in informative areas. The waypoints are used to form a finite number of high value candidate routes for the optimization. Second, the optimization speed is increased by using a specialized version of Thompson sampling. Thompson sampling is a search strategy that balances exploration and exploitation. Traditional Thompson sampling is modified to incorporate an upper bound on the success probability, reducing the time until convergence. Additionally, the simulation is structured to reuse data from previously-computed routes. TRN-RPM accurately estimates the success probability and greatly reduces the time required to plan routes for TRN missions. TRN-RPM was demonstrated using simulation and experimental dive results. The method was used to plan routes for two missions in Monterey Bay, California. The simulation results demonstrate convergence to routes with a high success probability in 1/100th of the time required for an exhaustive search of all candidate routes. One of these missions was experimentally demonstrated on an AUV operated by the Monterey Bay Aquarium Research Institute (MBARI). These demonstrations included several runs in which the AUV followed the optimized route as well as runs using several other non-optimized routes for comparison. They demonstrated that using TRN-RPM can effectively increase mission success probability in real-world TRN missions.
Download or read book Navigation and Control of Autonomous Marine Vehicles written by Sanjay Sharma and published by Institution of Engineering and Technology. This book was released on 2019-04-02 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robotic marine vessels can be used for a wide range of purposes, including defence, marine science, offshore energy and hydrographic surveys, and environmental surveys and protection. Such vessels need to meet a variety of criteria: they must be able to operate in salt water, and to communicate and be controlled over large distances, even when submerged or in inclement weather. Further challenges include 3D navigation of individual vehicles, groups or squadrons.
Download or read book Autonomous Underwater Vehicle Navigation and Mapping in Dynamic Unstructured Environments written by Clayton Gregory Kunz and published by . This book was released on 2012 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents a system for automatically building 3-D optical and bathymetric maps of underwater terrain using autonomous robots. The maps that are built improve the state of the art in resolution by an order of magnitude, while fusing bathymetric information from acoustic ranging sensors with visual texture captured by cameras. As part of the mapping process, several internal relationships between sensors are automatically calibrated, including the roll and pitch offsets of the velocity sensor, the attitude offset of the multibeam acoustic ranging sensor, and the full six-degree of freedom offset of the camera. The system uses pose graph optimization to simultaneously solve for the robot's trajectory, the map, and the camera location in the robot's frame, and takes into account the case where the terrain being mapped is drifting and rotating by estimating the orientation of the terrain at each time step in the robot's trajectory. Relative pose constraints are introduced into the pose graph based on multibeam submap matching using depth image correlation, while landmark-based constraints are used in the graph where visual features are available. The two types of constraints work in concert in a single optimization, fusing information from both types of mapping sensors and yielding a texture-mapped 3-D mesh for visualization. The optimization framework also allows for the straightforward introduction of constraints provided by the particular suite of sensors available, so that the navigation and mapping system presented works under a variety of deployment scenarios, including the potential incorporation of external localization systems such as long-baseline acoustic networks. Results of using the system to map the draft of rotating Antarctic ice floes are presented, as are results fusing optical and range data of a coral reef.
Download or read book Autonomous Underwater Vehicles written by Frank Ehlers and published by SciTech Publishing. This book was released on 2020-08-26 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gives a state-of-the-art overview of the hot topic of autonomous underwater vehicle (AUV) design and practice. It covers a wide range of AUV application areas such as education and research, biological and oceanographic studies, surveillance purposes, military and security applications and industrial underwater applications.
Download or read book Long Range Gravity Aided Autonomous Underwater Vehicle Navigation written by Franz Heubach and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous underwater vehicles (AUV) are a mobile platform for underwater sensing, an environment relatively unexplored. Georeferencing measurements is difficult due to the challenge of AUV localization. The rapid attenuation of radio frequencies underwater restricts AUVs from using the global position system (GPS), the above-water solution to localization. Underwater localization relies on dead-reckoning, the integration of vehicle inertia measurements to arrive at a position estimate. However, the dead-reckoned position error is unbounded. This error can be bounded using a source of position feedback. Terrain aided navigation (TAN) - using georeferenced geophysical terrain maps can provide that feedback. TAN shows significant promise as a method for long-range, passive underwater AUV navigation, especially gravity-aided navigation (GAN). This thesis presents a TAN algorithm that uses a gravity gradiometer and gravity gradient maps to successfully limit dead-reckoning error by a factor of 25 over a 500 km long AUV mission, with a localization accuracy of 1 km. The TAN algorithm exploits the correlation between terrain and the gravity anomaly to use a global database of bathymetry maps (GEBCO) with 400 m resolution. The mission was simulated in the AUV navigation testbed (ANT), a collection of tooling developed during this thesis to accelerate research in TAN. Among the contributions made by the ANT, is a inertial navigation system (INS) that emulates the uncertainty characteristics of a commercial navigation grade INS (Kearfott Seanav) \textemdash~to simulate dead-reckoning error growth. Parts of the ANT have been released to the research community as open-source, and are being used by researchers in the Intelligent Systems Laboratory (ISL) at Dalhousie University.
Download or read book Autonomous Underwater Vehicles written by Nuno Cruz and published by BoD – Books on Demand. This book was released on 2011-10-21 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous Underwater Vehicles (AUVs) are remarkable machines that revolutionized the process of gathering ocean data. Their major breakthroughs resulted from successful developments of complementary technologies to overcome the challenges associated with autonomous operation in harsh environments. Most of these advances aimed at reaching new application scenarios and decreasing the cost of ocean data collection, by reducing ship time and automating the process of data gathering with accurate geo location. With the present capabilities, some novel paradigms are already being employed to further exploit the on board intelligence, by making decisions on line based on real time interpretation of sensor data. This book collects a set of self contained chapters covering different aspects of AUV technology and applications in more detail than is commonly found in journal and conference papers. They are divided into three main sections, addressing innovative vehicle design, navigation and control techniques, and mission preparation and analysis. The progress conveyed in these chapters is inspiring, providing glimpses into what might be the future for vehicle technology and applications.
Download or read book Undersea Vehicles and National Needs written by Committee on Undersea Vehicles and National Needs and published by National Academies Press. This book was released on 1996-12-03 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: The United States faces decisions requiring information about the oceans in vastly expanded scales of time and space and from oceanic sectors not accessible with the suite of tools now used by scientists and engineers. Advances in guidance and control, communications, sensors, and other technologies for undersea vehicles can provide an opportunity to understand the oceans' influence on the energy and chemical balance that sustains humankind and to manage and deliver resources from and beneath the sea. This book assesses the state of undersea vehicle technology and opportunities for vehicle applications in science and industry. It provides guidance about vehicle subsystem development priorities and describes how national research can be focused most effectively.
Download or read book Autonomous Underwater Vehicle Navigation Relative to a Moving Target Ship Using Moving Long Baseline Navigation written by Amanda J. Folk and published by . This book was released on 2011 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robotics Research written by John M. Hollerbach and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the proceedings of the 9th International Symposium of Robotics Research, one of the oldest and most prestigious conferences in robotics. The goal of the symposium was to bring together active, leading robotics researchers from academia, government and industry, to define the state of the art of robotics and its future direction. The broad spectrum of robotics research is covered, with an eye on what will be important in robotics in the next millennium.
Download or read book Encyclopedia of Robotics written by Marcelo H. Ang and published by Springer. This book was released on 2018-07-13 with total page 4000 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Encyclopedia of Robotics addresses the existing need for an easily accessible yet authoritative and granular knowledge resource in robotic science and engineering. The encyclopedia is a work that comprehensively explains the scientific, application-based, interactive and socio-ethical parameters of robotics. It is the first work that explains at the concept and fact level the state of the field of robotics and its future directions. The encyclopedia is a complement to Springer’s highly successful Handbook of Robotics that has analyzed the state of robotics through the medium of descriptive essays. Organized in an A-Z format for quick and easy understanding of both the basic and advanced topics across a broad spectrum of areas in a self-contained form. The entries in this Encyclopedia will be a comprehensive description of terms used in robotics science and technology. Each term, when useful, is described concisely with online illustrations and enhanced user interactivity (on SpringerReference.com).
Download or read book Underwater Robots written by Gianluca Antonelli and published by Springer. This book was released on 2013-11-21 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with the state of the art in underwater robotics experiments of dynamic control of an underwater vehicle. The author presents experimental results on motion control and fault tolerance to thrusters’ faults with the autonomous vehicle ODIN. This second substantially improved and expanded edition new features are presented dealing with fault-tolerant control and coordinated control of autonomous underwater vehicles.
