Augmented Terrain-Based Navigation to Enable Persistent Autonomy for Underwater Vehicles in GPS-Denied Environments

Aquatic robots, such as Autonomous Underwater Vehicles (AUVs), play a major role in the study of ocean processes that require long-term sampling efforts and commonly perform navigation via dead-reckoning using an accelerometer, a magnetometer, a compass, an IMU and a depth sensor for feedback. However, these instruments are subjected to large drift, leading to unbounded uncertainty in location. Moreover, the spatio-temporal dynamics of the ocean environment, coupled with limited communication capabilities, make navigation and localization difficult, especially in coastal regions where the majority of interesting phenomena occur. To add to this, the interesting features are themselves spatio-temporally dynamic, and effective sampling requires a good understanding of vehicle localization relative to the sampled feature. Therefore, our work is motivated by the desire to enable intelligent data collection of complex dynamics and processes that occur in coastal ocean environments to further our understanding and prediction capabilities. The study originated from the need to localize and navigate aquatic robots in a GPS-denied environment and examine the role of the spatio-temporal dynamics of the ocean into the localization and navigation processes. The methods and techniques needed range from the data collection to the localization and navigation algorithms used on-board of the aquatic vehicles. The focus of this work is to develop algorithms for localization and navigation of AUVs in GPS-denied environments. We developed an Augmented terrain-based framework that incorporates physical science data, i.e., temperature, salinity, pH, etc., to enhance the topographic map that the vehicle uses to navigate. In this navigation scheme, the bathymetric data are combined with the physical science data to enrich the uniqueness of the underlying terrain map and increase the accuracy of underwater localization. Another technique developed in this work addresses the problem of tracking an underwater vehicle when the GPS signal suddenly becomes unavailable. The methods include the whitening of the data to reveal the true statistical distance between datapoints and also incorporates physical science data to enhance the topographic map. Simulations were performed at Lake Nighthorse, Colorado, USA, between April 25th and May 2nd 2018 and at Big Fisherman\u27s Cove, Santa Catalina Island, California, USA, on July 13th and July 14th 2016. Different missions were executed on different environments (snow, rain and the presence of plumes). Results showed that these two methodologies for localization and tracking work for reference maps that had been recorded within a week and the accuracy on the average error in localization can be compared to the errors found when using GPS if the time in which the observations were taken are the same period of the day (morning, afternoon or night). The whitening of the data had positive results when compared to localizing without whitening

Towards Autonomous Localization of an Underwater Drone

Autonomous vehicle navigation is a complex and challenging task. Land and aerial vehicles often use highly accurate GPS sensors to localize themselves in their environments. These sensors are ineffective in underwater environments due to signal attenuation. Autonomous underwater vehicles utilize one or more of the following approaches for successful localization and navigation: inertial/dead-reckoning, acoustic signals, and geophysical data. This thesis examines autonomous localization in a simulated environment for an OpenROV Underwater Drone using a Kalman Filter. This filter performs state estimation for a dead reckoning system exhibiting an additive error in location measurements. We evaluate the accuracy of this Kalman Filter by analyzing the effect each parameter has on accuracy, then choosing the best combination of parameter values to assess the overall accuracy of the Kalman Filter. We find that the two parameters with the greatest effects on the system are the constant acceleration and the measurement uncertainty of the system. We find the filter employing the best combination of parameters can greatly reduce measurement error and improve accuracy under typical operating conditions

Contributions to automated realtime underwater navigation

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 2012This dissertation presents three separate–but related–contributions to the art of underwater navigation. These methods may be used in postprocessing with a human in the loop, but the overarching goal is to enhance vehicle autonomy, so the emphasis is on automated approaches that can be used in realtime. The three research threads are: i) in situ navigation sensor alignment, ii) dead reckoning through the water column, and iii) model-driven delayed measurement fusion. Contributions to each of these areas have been demonstrated in simulation, with laboratory data, or in the field–some have been demonstrated in all three arenas. The solution to the in situ navigation sensor alignment problem is an asymptotically stable adaptive identifier formulated using rotors in Geometric Algebra. This identifier is applied to precisely estimate the unknown alignment between a gyrocompass and Doppler velocity log, with the goal of improving realtime dead reckoning navigation. Laboratory and field results show the identifier performs comparably to previously reported methods using rotation matrices, providing an alignment estimate that reduces the position residuals between dead reckoning and an external acoustic positioning system. The Geometric Algebra formulation also encourages a straightforward interpretation of the identifier as a proportional feedback regulator on the observable output error. Future applications of the identifier may include alignment between inertial, visual, and acoustic sensors. The ability to link the Global Positioning System at the surface to precision dead reckoning near the seafloor might enable new kinds of missions for autonomous underwater vehicles. This research introduces a method for dead reckoning through the water column using water current profile data collected by an onboard acoustic Doppler current profiler. Overlapping relative current profiles provide information to simultaneously estimate the vehicle velocity and local ocean current–the vehicle velocity is then integrated to estimate position. The method is applied to field data using online bin average, weighted least squares, and recursive least squares implementations. This demonstrates an autonomous navigation link between the surface and the seafloor without any dependence on a ship or external acoustic tracking systems. Finally, in many state estimation applications, delayed measurements present an interesting challenge. Underwater navigation is a particularly compelling case because of the relatively long delays inherent in all available position measurements. This research develops a flexible, model-driven approach to delayed measurement fusion in realtime Kalman filters. Using a priori estimates of delayed measurements as augmented states minimizes the computational cost of the delay treatment. Managing the augmented states with time-varying conditional process and measurement models ensures the approach works within the proven Kalman filter framework–without altering the filter structure or requiring any ad-hoc adjustments. The end result is a mathematically principled treatment of the delay that leads to more consistent estimates with lower error and uncertainty. Field results from dead reckoning aided by acoustic positioning systems demonstrate the applicability of this approach to real-world problems in underwater navigation.I have been financially supported by: the National Defense Science and Engineering Graduate (NDSEG) Fellowship administered by the American Society for Engineering Education, the Edwin A. Link Foundation Ocean Engineering and Instrumentation Fellowship, and WHOI Academic Programs office

Visually Augmented Navigation in an Unstructured Environment Using a Delayed State History

This paper describes a framework for sensor fusion of navigation data with camera-based 5 DOF relative pose measurements for 6 DOF vehicle motion in an unstructured 3D underwater environment. The fundamental goal of this work is to concurrently sstimate online current vehicle position and its past trajectory. This goal is framed within the context of improving mobile robot navigation to support sub-sea science and exploration. Vehicle trajectory is represented by a history of poses in an augmented state Kalman filter. Camera spatial constraints from overlapping imagery provide partial observation of these posa and are used to enforce consislency and provide a mechanism for loop-closure. The multi-sensor camera+navigation framework is shown to have compelling advantages over a camera-only based approach by 1) improving the robustness of pairwise image registration, 2) setting the free gauge scale, and 3) allowing for a unconnected camera graph topology. Results are shown for a real world data set collected by an autonomous underwater vehicle in an unstructured undersea environment.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86055/1/reustice-32.pd

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter

The Kalman filter variants extended Kalman filter (EKF) and error-state Kalman filter (ESKF) are widely used in underwater multi-sensor fusion applications for localization and navigation. Since these filters are designed by employing first-order Taylor series approximation in the error covariance matrix, they result in a decrease in estimation accuracy under high nonlinearity. In order to address this problem, we proposed a novel multi-sensor fusion algorithm for underwater vehicle localization that improves state estimation by augmentation of the radial basis function (RBF) neural network with ESKF. In the proposed algorithm, the RBF neural network is utilized to compensate the lack of ESKF performance by improving the innovation error term. The weights and centers of the RBF neural network are designed by minimizing the estimation mean square error (MSE) using the steepest descent optimization approach. To test the performance, the proposed RBF-augmented ESKF multi-sensor fusion was compared with the conventional ESKF under three different realistic scenarios using Monte Carlo simulations. We found that our proposed method provides better navigation and localization results despite high nonlinearity, modeling uncertainty, and external disturbances.This research was partially funded by the Campus de Excelencia Internacional Andalucia Tech, University of Malaga, Malaga, Spain. Partial funding for open access charge: Universidad de Málag

A survey on uninhabited underwater vehicles (UUV)

ASME Early Career Technical Conference, ASME ECTC, October 2-3, 2009, Tuscaloosa, Alabama, USAThis work presents the initiation of our underwater robotics research which will be focused on underwater vehicle-manipulator systems. Our aim is to build an underwater vehicle with a robotic manipulator which has a robust system and also can compensate itself under the influence of the hydrodynamic effects. In this paper, overview of the existing underwater vehicle systems, thruster designs, their dynamic models and control architectures are given. The purpose and results of the existing methods in underwater robotics are investigated

Towards Odor-Sensitive Mobile Robots

J. Monroy, J. Gonzalez-Jimenez, "Towards Odor-Sensitive Mobile Robots", Electronic Nose Technologies and Advances in Machine Olfaction, IGI Global, pp. 244--263, 2018, doi:10.4018/978-1-5225-3862-2.ch012 Versión preprint, con permiso del editorOut of all the components of a mobile robot, its sensorial system is undoubtedly among the most critical ones when operating in real environments. Until now, these sensorial systems mostly relied on range sensors (laser scanner, sonar, active triangulation) and cameras. While electronic noses have barely been employed, they can provide a complementary sensory information, vital for some applications, as with humans. This chapter analyzes the motivation of providing a robot with gas-sensing capabilities and also reviews some of the hurdles that are preventing smell from achieving the importance of other sensing modalities in robotics. The achievements made so far are reviewed to illustrate the current status on the three main fields within robotics olfaction: the classification of volatile substances, the spatial estimation of the gas dispersion from sparse measurements, and the localization of the gas source within a known environment