Autonomous trajectory design system for mapping of unknown sea-floors using a team of AUVs

This research develops a new on-line trajectory planning algorithm for a team of Autonomous Underwater Vehicles (AUVs). The goal of the AUVs is to cooperatively explore and map the ocean seafloor. As the morphology of the seabed is unknown and complex, standard non-convex algorithms perform insufficiently. To tackle this, a new simulationbased approach is proposed and numerically evaluated. This approach adapts the Parametrized Cognitive-based Adaptive Optimization (PCAO) algorithm. The algorithm transforms the exploration problem to a parametrized decision-making mechanism whose real-time implementation is feasible. Upon that transformation, this scheme calculates off-line a set of decision making mechanism’s parameters that approximate the - nonpractically feasible - optimal solution. The advantages of the algorithm are significant computational simplicity, scalability, and the fact that it can straightforwardly embed any type of physical constraints and system limitations. In order to train the PCAO controller, two morphologically different seafloors are used. During this training, the algorithm outperforms an unrealistic optimal-one-step-ahead search algorithm. To demonstrate the universality of the controller, the most effective controller is used to map three new morphologically different seafloors. During the latter mapping experiment, the PCAO algorithm outperforms several gradient-descent-like approaches

Closed‐loop one‐way‐travel‐time navigation using low‐grade odometry for autonomous underwater vehicles

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of FIeld Robotics 35 (2018): 421-434, doi:10.1002/rob.21746.This paper extends the progress of single beacon one‐way‐travel‐time (OWTT) range measurements for constraining XY position for autonomous underwater vehicles (AUV). Traditional navigation algorithms have used OWTT measurements to constrain an inertial navigation system aided by a Doppler Velocity Log (DVL). These methodologies limit AUV applications to where DVL bottom‐lock is available as well as the necessity for expensive strap‐down sensors, such as the DVL. Thus, deep water, mid‐water column research has mostly been left untouched, and vehicles that need expensive strap‐down sensors restrict the possibility of using multiple AUVs to explore a certain area. This work presents a solution for accurate navigation and localization using a vehicle's odometry determined by its dynamic model velocity and constrained by OWTT range measurements from a topside source beacon as well as other AUVs operating in proximity. We present a comparison of two navigation algorithms: an Extended Kalman Filter (EKF) and a Particle Filter(PF). Both of these algorithms also incorporate a water velocity bias estimator that further enhances the navigation accuracy and localization. Closed‐loop online field results on local waters as well as a real‐time implementation of two days field trials operating in Monterey Bay, California during the Keck Institute for Space Studies oceanographic research project prove the accuracy of this methodology with a root mean square error on the order of tens of meters compared to GPS position over a distance traveled of multiple kilometers.This work was supported in part through funding from the Weston Howland Jr. Postdoctoral Scholar Award (BCC), the U.S. Navy's Civilian Institution program via the MIT/WHOI Joint Program (JHK),W. M. Keck Institute for Space Studies, and theWoods Hole Oceanographic Institution

Design and testing of a position adaptation system for KUKA robots using photoelectric sensors

This thesis presents the development and analysis of a position monitoring and adaptation system to be used in conjunction with a KUKA KR16-2 articulated robot using components readily available in most manufacturing settings. This system could be beneficial in the manufacturing sector in areas such as polymer welding and spray painting. In the former it could be used to maintain an effective distance between a welding end effector laying molten plastic and the surface area of the parts being welded, or in the case of the latter the system would be useful in painting objects of unknown shape or objects with unknown variations in the surface level. In the case of spray painting if you spray to close to an object you will get an inconsistent amount of paint applied to an area. This system would maintain the programmed distance between the robot system and target object. Typically, systems that achieve this level of control rely on expensive sensors such as force torque sensors. This research proposes to take the first step in trying to address the technical problems by introducing a novel way of adapting to a target surface deformation using comparably low cost photoelectric diffuse sensors. The key outcomes of this thesis can be found in the form of a software package to interface the photo-electric sensors to the KUKA robot system. This system is operated by a custom-built algorithm which is capable of dynamically calculating robot movements based off the sensor input. Additionally, an optimum system setup is developed with different configurations of sensor mounting and speeds of robot operation discussed and tested. The viability of the photo-electric diffuses sensors used in this application is also considered with further works suggested. Finally, a secondary application is developed for recording and analysing KUKA robot movements for use in other research activities

Control and visual navigation for unmanned underwater vehicles

Ph. D. Thesis.Control and navigation systems are key for any autonomous robot. Due to environmental disturbances, model uncertainties and nonlinear dynamic systems, reliable functional control is essential and improvements in the controller design can significantly benefit the overall performance of Unmanned Underwater Vehicles (UUVs). Analogously, due to electromagnetic attenuation in underwater environments, the navigation of UUVs is always a challenging problem. In this thesis, control and navigation systems for UUVs are investigated. In the control field, four different control strategies have been considered: Proportional-Integral-Derivative Control (PID), Improved Sliding Mode Control (SMC), Backstepping Control (BC) and customised Fuzzy Logic Control (FLC). The performances of these four controllers were initially simulated and subsequently evaluated by practical experiments in different conditions using an underwater vehicle in a tank. The results show that the improved SMC is more robust than the others with small settling time, overshoot, and error. In the navigation field, three underwater visual navigation systems have been developed in the thesis: ArUco Underwater Navigation systems, a novel Integrated Visual Odometry with Monocular camera (IVO-M), and a novel Integrated Visual Odometry with Stereo camera (IVO-S). Compared with conventional underwater navigation, these methods are relatively low-cost solutions and unlike other visual or inertial-visual navigation methods, they are able to work well in an underwater sparse-feature environment. The results show the following: the ArUco underwater navigation system does not suffer from cumulative error, but some segments in the estimated trajectory are not consistent; IVO-M suffers from cumulative error (error ratio is about 3 - 4%) and is limited by the assumption that the “seabed is locally flat”; IVO-S suffers from small cumulative errors (error ratio is less than 2%). Overall, this thesis contributes to the control and navigation systems of UUVs, presenting the comparison between controllers, the improved SMC, and low-cost underwater visual navigation methods

Toward lifelong visual localization and mapping

Thesis (Ph.D.)--Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science; and the Woods Hole Oceanographic Institution), 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 171-181).Mobile robotic systems operating over long durations require algorithms that are robust and scale efficiently over time as sensor information is continually collected. For mobile robots one of the fundamental problems is navigation; which requires the robot to have a map of its environment, so it can plan its path and execute it. Having the robot use its perception sensors to do simultaneous localization and mapping (SLAM) is beneficial for a fully autonomous system. Extending the time horizon of operations poses problems to current SLAM algorithms, both in terms of robustness and temporal scalability. To address this problem we propose a reduced pose graph model that significantly reduces the complexity of the full pose graph model. Additionally we develop a SLAM system using two different sensor modalities: imaging sonars for underwater navigation and vision based SLAM for terrestrial applications. Underwater navigation is one application domain that benefits from SLAM, where access to a global positioning system (GPS) is not possible. In this thesis we present SLAM systems for two underwater applications. First, we describe our implementation of real-time imaging-sonar aided navigation applied to in-situ autonomous ship hull inspection using the hovering autonomous underwater vehicle (HAUV). In addition we present an architecture that enables the fusion of information from both a sonar and a camera system. The system is evaluated using data collected during experiments on SS Curtiss and USCGC Seneca. Second, we develop a feature-based navigation system supporting multi-session mapping, and provide an algorithm for re-localizing the vehicle between missions. In addition we present a method for managing the complexity of the estimation problem as new information is received. The system is demonstrated using data collected with a REMUS vehicle equipped with a BlueView forward-looking sonar. The model we use for mapping builds on the pose graph representation which has been shown to be an efficient and accurate approach to SLAM. One of the problems with the pose graph formulation is that the state space continuously grows as more information is acquired. To address this problem we propose the reduced pose graph (RPG) model which partitions the space to be mapped and uses the partitions to reduce the number of poses used for estimation. To evaluate our approach, we present results using an online binocular and RGB-Depth visual SLAM system that uses place recognition both for robustness and multi-session operation. Additionally, to enable large-scale indoor mapping, our system automatically detects elevator rides based on accelerometer data. We demonstrate long-term mapping using approximately nine hours of data collected in the MIT Stata Center over the course of six months. Ground truth, derived by aligning laser scans to existing floor plans, is used to evaluate the global accuracy of the system. Our results illustrate the capability of our visual SLAM system to map a large scale environment over an extended period of time.by Hordur Johannsson.Ph.D

Robot Navigation in Distorted Magnetic Fields

This thesis investigates the utilization of magnetic field distortions for the localization and navigation of robotic systems. The work comprehensively illuminates the various aspects that are relevant in this context. Among other things, the characteristics of magnetic field environments are assessed and examined for their usability for robot navigation in various typical mobile robot deployment scenarios. A strong focus of this work lies in the self-induced static and dynamic magnetic field distortions of complex kinematic robots, which could hinder the use of magnetic fields because of their interference with the ambient magnetic field. In addition to the examination of typical distortions in robots of different classes, solutions for compensation and concrete tools are developed both in hardware (distributed magnetometer sensor systems) and in software. In this context, machine learning approaches for learning static and dynamic system distortions are explored and contrasted with classical methods for calibrating magnetic field sensors. In order to extend probabilistic state estimation methods towards the localization in magnetic fields, a measurement model based on Mises-Fisher distributions is developed in this thesis. Finally, the approaches of this work are evaluated in practice inside and outside the laboratory in different environments and domains (e.g. office, subsea, desert, etc.) with different types of robot systems

Underwater & out of sight: towards ubiquity in underwater robotics

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 September 2019.The Earth's oceans holds a wealth of information currently hidden from us. Effective measurement of its properties could provide a better understanding of our changing climate and insights into the creatures that inhabit its waters. Autonomous underwater vehicles (AUVs) hold the promise of penetrating the ocean environment and uncovering its mysteries; and progress in underwater robotics research over the past three decades has resulted in vehicles that can navigate reliably and operate consistently, providing oceanographers with an additional tool for studying the ocean. Unfortunately, the high cost of these vehicles has stifled the democratization of this technology. We believe that this is a consequence of two factors. Firstly, reliable navigation on conventional AUVs has been achieved through the use of a sophisticated sensor system, namely the Doppler velocity log (DVL)-aided inertial navigation system (INS), which drives up vehicle cost, power use and size. Secondly, deployment of these vehicles is expensive and unwieldy due to their complexity, size and cost, resulting in the need for specialized personnel for vehicle operation and maintenance. The recent development of simpler, low-cost, miniature underwater robots provides a solution that mitigates both these factors; however, removing the expensive DVL-aided INS means that they perform poorly in terms of navigation accuracy. We address this by introducing a novel acoustic system that enables AUV self-localization without requiring a DVL-aided INS or on-board active acoustic transmitters. We term this approach Passive Inverted Ultra-Short Baseline (piUSBL) positioning. The system uses a single acoustic beacon and a time-synchronized, vehicle-mounted, passive receiver array to localize the vehicle relative to this beacon. Our approach has two unique advantages: first, a single beacon lowers cost and enables easy deployment; second, a passive receiver allows the vehicle to be low-power, low-cost and small, and enables multi-vehicle scalability. Providing this new generation of small and inexpensive vehicles with accurate navigation can potentially lower the cost of entry into underwater robotics research and further its widespread use for ocean science. We hope that these contributions in low-cost underwater navigation will enable the ubiquitous and coordinated use of robots to explore and understand the underwater domain.This research was funded and supported by a number of sponsors; we gratefully acknowledge them below. Defense Advanced Research Projects Agency (DARPA) and SSC Pacific via Applied Physical Sciences Corp. (APS) under contract number N66001-11-C-4115. SSC Pacific via Applied Physical Sciences Corp. (APS) under award number N66001-14-C-4031. Air Force via Lincoln Laboratory under award number FA8721-05-C-0002. Office of Naval Research (ONR) via University of California-San Diego under award number N00014-13-1-0632. Defense Advanced Research Projects Agency (DARPA) via Applied Physical Sciences Corp. (APS) under award number HR0011-18-C-0008. Office of Naval Research (ONR) under award number N00014-17-1-2474

Towards full-scale autonomy for multi-vehicle systems planning and acting in extreme environments

Currently, robotic technology offers flexible platforms for addressing many challenging problems that arise in extreme environments. These problems’ nature enhances the use of heterogeneous multi-vehicle systems which can coordinate and collaborate to achieve a common set of goals. While such applications have previously been explored in limited contexts, long-term deployments in such settings often require an advanced level of autonomy to maintain operability. The success of planning and acting approaches for multi-robot systems are conditioned by including reasoning regarding temporal, resource and knowledge requirements, and world dynamics. Automated planning provides the tools to enable intelligent behaviours in robotic systems. However, whilst many planning approaches and plan execution techniques have been proposed, these solutions highlight an inability to consistently build and execute high-quality plans. Motivated by these challenges, this thesis presents developments advancing state-of-the-art temporal planning and acting to address multi-robot problems. We propose a set of advanced techniques, methods and tools to build a high-level temporal planning and execution system that can devise, execute and monitor plans suitable for long-term missions in extreme environments. We introduce a new task allocation strategy, called HRTA, that optimises the task distribution amongst the heterogeneous fleet, relaxes the planning problem and boosts the plan search. We implement the TraCE planner that enforces contingent planning considering propositional temporal and numeric constraints to deal with partial observability about the initial state. Our developments regarding robust plan execution and mission adaptability include the HLMA, which efficiently optimises the task allocation and refines the planning model considering the experience from robots’ previous mission executions. We introduce the SEA failure solver that, combined with online planning, overcomes unexpected situations during mission execution, deals with joint goals implementation, and enhances mission operability in long-term deployments. Finally, we demonstrate the efficiency of our approaches with a series of experiments using a new set of real-world planning domains.Engineering and Physical Sciences Research Council (EPSRC) grant EP/R026173/