1,721,006 research outputs found
Total energy shaping of a class of underactuated port-hamiltonian systems using a new set of closedloop potential shape variables
No full textAbstract — This paper proposes a method for designing set-point regulation controllers for a class of underactuated me-chanical systems in Port-Hamiltonian System (PHS) form. A new set of potential shape variables in closed loop is proposed, which can replace the set of open loop shape variables— the configuration variables that appear in the kinetic energy. With this choice, the closed-loop potential energy contains free functions of the new variables. By expressing the regulation objective in terms of these new potential shape variables, the desired equilibrium can be assigned and there is freedom to reshape the potential energy to achieve performance whilst maintaining the PHS form in closed loop. This complements contemporary results in the literature, which preserve the open-loop shape variables. As a case study, we consider a robotic manipulator mounted on a flexible base and compensate for the motion of the base while positioning the end effector with respect to the ground reference. We compare the proposed control strategy with special cases that correspond to other energy shaping strategies previously proposed in the literature. I
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Outlier Accommodation in Sensor Rich Environments by Risk-Averse Performance-Specified State Estimation
Get PDFMany applications require reliable, high precision state estimation while mitigating measurement outliers. This dissertation presents a novel state estimation approach to the challenge of preventing outlier measurements from affecting the accuracy and reliability of state estimation. Since outliers can degrade the performance of state estimation, outlier accommodation is critical. The most common method for outlier accommodation utilizes a Neyman-Pearson (NP) type threshold test in a (extended) Kalman filter (KF) to detect and remove residuals greater than a designer specified threshold. Such threshold based methods may use residuals arbitrarily close to the threshold, even when they are not needed to achieve an application's performance specification. Outlier measurements that pass the residual test (i.e., missed detections) results in incorrect information being incorporated into the state and error covariance estimates. Once the state and covariance are incorrect, subsequent outlier decisions may be incorrect, possibly causing divergence.The major contribution of this dissertation is changing the focus from outlier detection, to looking for a subset of measurements which have minimum risk while achieving a lower bounded information for state estimation. Risk-averse performance-specified (RAPS) state estimation works within an optimization setting to choose a set of measurements that achieves a performance specification with minimum risk of outlier inclusion. This dissertation derives and formulates the RAPS solution for outlier accommodation which applies to both linear and nonlinear applications. The approach is also extended to moving horizon state estimation problem. Global Navigation Satellite Systems (GNSS) and inertial measurements for moving vehicle state estimation are used as an example to show the performance of the proposed approach
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Frame Definition, Pose Initialization, and Real-Time Localization in a Non-Stationary Reference Frame With LiDAR and IMU: Application in Cooperative Transloading Tasks
Get PDFCargo transloading is an important part of the transportation, and the increasing needs of transportation is a challenge to the transloading capacity. Therefore, automating the process to reduce the manpower consumption and improve the efficiency of transloading is of interest. Cooperative transloading is a common method for bulk cargo transloading, which refers to a robot inside the container that transfers the bulk cargo to a convenient position that the crane can reach. Cooperation improves the crane grabbing efficiency, and avoids the bulk cargo being in the container areas that the crane cannot reach. In this dissertation, a method is proposed to recognize and locate the container hatch from point cloud, to unify the reference frame of the crane and robot, to determine the initial robot pose in that shared frame, and to localize the robot as it maneuvers in the container in real-time during the transloading. The main contributions of this research fit into two categories.Frame Definition and Initial Pose Determination: To establish a reference frame recognizable by both the crane and the robot, a portion of this dissertation focus on extracting the hatch from the robot point cloud. One hatch corner and the hatch edges define the origin and axis of the shared working frame for the robot and crane. To find the hatch, the 3D point cloud scanned by the robot is rasterized into 2D data, preserving the relative position information of the hatch. A method based on the Hough Transform is used to determine the initial point cloud translation and rotation with respect to the hatch using the 2D data. With the determined translation and rotation, the common reference frame is defined, the point cloud is re-coordinatized into this frame and the initial robot pose can be determined and expressed in this frame.Real-Time Localization: The transloading starts after determining the robot initial pose. The robot moves in the container. To avoid collisions, the robot real-time position needs to be reported to the crane. In this dissertation, a basemap is created initially using the point cloud scanned for determining the initial pose. Later, when the robot moves, its LiDAR scans are matched with the basemap using an ICP algorithm to determine the robot pose in real-time. To achieve more reliable matching results with the ICP algorithm, several approaches are compared for roughly aligning the real-time scans to the basemap before using ICP algorithm, including the use of LiDAR-estimated poses and velocities, and the use of IMU measurements to calculate the pose change. The experimental comparisons of those methods are assessed to determine the most suitable one for cooperative transloading.In addition to the analysis and development of new methods for hatch recognition, cooperative frame definition, and real-time localization in a non-stationary frame, this research has developed a fully functional real-time prototype implementation
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Optimization-Based Risk-Averse Outlier Accommodation With Linear Performance Constraints: Real-Time Computation and Constraint Feasibility in CAV State Estimation
Get PDFConnected and Autonomous Vehicles (CAV) require positioning that is consistently reliable and accurate. This is achieved through the choice of sensors and the real-time selection of high-quality measurements. Global Navigation Satellite Systems (GNSS) are the foundation to achieve accurate absolute positioning. GNSS Common-mode Errors (CME)mitigation can be realized with Differential GNSS (DGNSS) approach and Precise Point Positioning (PPP) techniques. With the evolution of the International GNSS Service (IGS) Multi-GNSS Experiment (MGEX), Real-time PPP (RT-PPP) corrections for multi-GNSS have only recently become accessible. GNSS measurements are prone to outliers. This results in an inherent performance versus risk trade-off in CAV state estimation applications. Recently proposed Risk-Averse Performance Specified (RAPS) methods address this trade-off by optimally selecting a subset of measurements to minimize risk while achieving a target performance. The existing RAPS literature presents cases where the performance specification is stated for the full information matrix. However, those methods are not computationally efficient as required for real-time and do not address situations where that specification is infeasible.
This dissertation focuses on the Diagonal Performance-Specified RAPS (DiagRAPS) formulation. This dissertation begins with a review of GNSS measurement models and real-time CME mitigation techniques, such as DGNSS, PPP, and Virtual Network DGNSS (VN-DGNSS). It then develops the theory of DiagRAPS for both binary and non-binary measurement selection variables. Algorithms suitable for real-time applications are proposed within Linear Programming (LP) and Mixed-Integer Linear Programming (ILP) optimization frameworks, achieving polynomial time complexity. The convergence and computation costs of these algorithms are discussed. For binary DiagRAPS, a novel convex reformulation is derived, leading to a globally optimal solution that can be solved using existing tools. Additionally, a soft constraint optimization approach is proposed for situations when the specified performance is unfeasible. Finally, this dissertation evaluates DiagRAPS state estimation approaches using real-world multi-GNSS data from challenging environments for both DGNSS and RT-PPP applications. The results reveal that the locally optimal approach achieves state estimation performance comparable to the global solution. Both binary and non-binary DiagRAPS outperform traditional methods. Notably, the non-binary approach yielded the lowest computation cost and the best overall performance
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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Calibration of Multi-LIDAR Systems for Real-Time Surface Management: Application in Bucket Wheel Reclaimer
Get PDFStockpile reclaiming using a Bucket Wheel Reclaimer (BWR) is an important part of stockyard management. The growth in demand for material handling over the years has drawn attention to improve the automation of the process. However, studies have shown that stockpiled products are being reclaimed at approximately 50% of their potential. This study focuses on the challenges in the automation of stockyard management system using a BWR.For high accuracy point cloud computation and surface reconstruction of the stockpiled materials, accurate calibration is of crucial importance. This dissertation presents a calibration technique for multi-LIDAR systems to estimate the GNSS-LIDAR extrinsic parameters of BWR's. The approach presented works with one or more 2D LIDARs and does not require special markers (e.g., reflective tape) or surveyed locations (other than a DGNSS base station antenna). The method and its accuracy have been demonstrated using experimental data from a stockyard environment.Regarding real-time management and control, the dissertation presents a technique for real-time point cloud management, visualization, and feature extraction for large scale stockyards environments. The software solution described continuously manages the point cloud in real-time as the sensors stream data. It also displays the current stockpile on an interactive interface that allows the user to see the surface from different view-points, interrogate the coordinates of any surface location, and computes the BWR entry and exit point for automated operation. The software is tested using experimental data from a port located in Yantai, China
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Reliable GPS Integer Ambiguity Resolution
Get PDFTo operate, guide and control vehicles in low visibility conditions, it is critical that the states of the vehicle are accurately estimated, which includes the three dimensional position, velocity, and attitude. This can be accomplished by GPS (Global Positioning System) aided encoder or GPS aided inertial approaches. The overall positioning accuracy of either approach will be determined by the GPS performance. Real-time centimeter accuracy GPS positioning can be achieved using carrier phase measurements. This requiresfast and reliable on-the-fly integer ambiguity resolution.In this dissertation, we focus on resolving GPS ambiguity problem, including both integer ambiguity estimation and integer ambiguity validation. For integer ambiguity estimation, a brief overview of pervious work on integer ambiguity resolution is first presented.Then, an improved integer ambiguity resolution method is proposed. Subsequently, simulations and real-world data are presented to demonstrate the effectiveness of the method. We also present integer ambiguity algorithms with auxiliary measurements and algorithms with multiple epoch measurements, both of which are useful in GPS challenging areas. For integer ambiguity validation, a brief overview is first presented, and then analytic discussion and test results on several popular validations methods are studied. Finally we discuss GPS modernization and its effect on integer validation
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Unifying Behavior Based Control Design and Hybrid Stability Theory for AUV Application
Get PDFAutonomous Underwater Vehicles (AUVs) are extensively being used by the scientific, oil and gas, and military communities.Many of the missions require the vehicle to function in complex, cluttered environments; to react to changing environmental parameters; and, to find a collision-free path through a workspace containing a significant number of obstacles. Many of the AUV missions currently involve high risk for human lives and excessive costs. State-of-art vehicles are not maneuverable enough to successfully accomplish most of the desired tasks. Desirable vehicle control capabilities include the ability to drive at very low, controllable speeds, the ability to maintain a set distance and attitude (pitch and roll) relative to some surface for optimal sensor (both sonar and video) effectiveness, and the ability for the operator to intervene to change the mission activities. Moreover, a vehicle capable of rotating in place or having a fraction of a meter turning radius is needed to conduct desired missions. Novel controllers to implement these specific behaviors are expected to be nonlinear due, for example, to the fact that the vehicle is maneuvering at nonzero attitude while translating parallel to the surface. A specific mission that this research addresses is ship hull inspection. This dissertation works through the details of a method to control the vehicle's attitude and translation relative to a surface. The surface of interest for example being a ship hull.This dissertation describes the derivation, design, simulation, and implementation of a Behavior Based control system.Each behavior is designed using a command filtered backstepping (CFBS) approach. Each behavior and the switching among behaviors is provably stable in the sense of Lyapunov. We use the results from Hybrid System Control in order to prove stability during behavior switching, and thus the overall control system stability.This dissertation presents the simulation and in-water testing results of our control design applied to an AUV
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High Accuracy Sensor Aided Inertial Navigation Systems
Get PDFReliable and high accuracy (decimeter level) localization of a rover relative to a defined frame is an enabling technology for numerous Intelligent Transportation Systems (ITS) applications (e.g., automotive guidance, routing, lane departure warning). The goal of localization is to compute the navigation state of the rover in some defined frame of reference such that the expected errors in the estimate is within a given performance specification. Inertial navigation is a popular navigation technique since it provides full six Degree-Of-Freedom (DOF) navigation information. Further inertial sensors have been studied for decades and have well understood error models. This dissertation discusses the theoretical and implementation aspects of certain sensor aided Inertial Navigation Systems (INS). Though the presentation can be easily generalized to all forms of INS, the primary focus of this dissertation will be on automotive INS. This dissertation formulates the localization problem in a mathematically rigorous fashion and poses it as a nonlinear Bayesian estimation problem. The INS kinematic equations and linearized error state equations required by the Bayesian estimation solution are derived. Aiding techniques like GPS, Vision and stationary aiding are described and mathematically formulated. Observability and performance analysis are presented for each of these aiding scenarios. The last part of the dissertation defines and formulates the Near Real Time estimation problem
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