Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

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Computer Vision: Algorithms and Applications

We present a real-time algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from mobile robotics to the "pure vision" domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible to structure from motion approaches. The core of the approach is the online creation of a sparse but persistent map of natural landmarks within a probabilistic framework. Our key novel contributions include an active approach to mapping and measurement, the use of a general motion model for smooth camera movement, and solutions for monocular feature initialization and feature orientation estimation. Together, these add up to an extremely efficient and robust algorithm which runs at 30 Hz with standard PC and camera hardware. This work extends the range of robotic systems in which SLAM can be usefully applied, but also opens up new areas. We present applications of MonoSLAM to real-time 3D localization and mapping for a high-performance full-size humanoid robot and live augmented reality with a hand-held camera

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MonoSLAM: Real-Time Single Camera SLAM

This paper describes the simultaneous localization and mapping (SLAM) problem and the essential methods for solving the SLAM problem and summarizes key implementations and demonstrations of the method. While there are still many practical issues to overcome, especially in more complex outdoor environments, the general SLAM method is now a well understood and established part of robotics. Another part of the tutorial summarized more recent works in addressing some of the remaining issues in SLAM, including computation, feature representation, and data association

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Simultaneous localization and mapping: part I

Predicting Force Redistributions on Walking Robots for Reliable Locomotion: Modeling and Experiments

This paper presents a framework for autonomous robots that reason from range data. We argue for spatial reasoning as a basic cognition mode for robots operating in unpredictable work environments and present a three-level architecture for modeling and planning from range data. Two implementations, robotic excavation with sonar ranging and mine navigation with laser ranging, illustrate the techniques and provide two experiences to evaluate the architecture.

An architecture and two cases in range-based modeling and planning

Robotic exploration of remote areas to assist or replace human exploration reduces the cost, hazard and tedium of such exploration. For remote explorers, power is the most critical resource, and the most common source of that power is solar energy. Information about the robot configuration, the planned path, the terrain and the position of the sun can be processed by a solar power expert software module to calculate the power provided by a given plan of action. Using this information to select the best plans will enable remote robotic explorers to extend their lifetimes. This paper presents the development of a solar power expert and its implementation on a simulator. Several patterned path plans are evaluated with various solar panel configurations, starting times and locations, concentrating on polar regions.

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Solar Power Expert for Remote Robotic Explorers

Lunar ice, present at the poles, could be a source of water for drinking and growing crops, generating oxygen for breathing, and producing propellants for venturing beyond the moon to deep space. Viability of this grand vision depends on specifics of the accessibility, depth and concentration of the ice, which can only be determined by surface missions. This requires a campaign of diverse, repeated robotic explorations over time. This paper profiles MoonRanger, a micro-rover manifested on a 2022 NASA Commercial Lander Payload Services (CLPS) flight, which will be the first such mission, with its technologies, designs and operations for a pioneering in-situ measurement of lunar ice. The paper addresses the challenges of and solutions for micro-rover perception in darkness, navigation and mapping autonomy with limited computing, mechatronic miniaturization, mobility in polar terrain, thermal management, power generation, and others. MoonRanger's mission principles, designs, analyses and performance of the innovations, component technologies, rover, system and mission. Details covering rover design, its performance, and system elements such as stow-deploy, power, avionics, thermal, software and communication scenarios are articulated. The paper describes principles, instrument, and means for measuring ice. System considerations such as ConOps, ground control, and risk posture are addressed. The paper touches on the creative, operational, lean and resourceful development culture that is producing this. The paper concludes by discussing the “firsts”, the impacts of the technical contributions, the status of the development, and the path to mission.

Micro Rover Mission for Measuring Lunar Polar Ice

The 2005 DARPA Grand Challenge, a 212-kilometer race through the Mojave Desert, showcased the state of the art in high-speed, autonomous navigation of trails and roads. To win the challenge, a team's robot had to complete the course faster than any other robot, and it had to do so within 10 hours. Carnegie Mellon University's Red Team developed two robots, which used a combination of autonomous and human preplanning to become two of only four robots to complete the Grand Challenge. The robots used onboard sensors to adjust a preplanned route to avoid obstacles and correct for position-estimation errors. To be successful, teams had to develop innovative algorithms and systems - and rigorously test them to verify performance. The Red Team used the tests regressively to evaluate how unit changes in hardware and software affected the robots' overall driving ability

William Whittaker

Papers

Predicting Force Redistributions on Walking Robots for Reliable Locomotion: Modeling and Experiments

An architecture and two cases in range-based modeling and planning

Solar Power Expert for Remote Robotic Explorers

Micro Rover Mission for Measuring Lunar Polar Ice

Testing driver skill for high-speed autonomous vehicles