6-DoF Navigation Systems for Autonomous Underwater Vehicles

Austri

Real-Time Indoor Localization using Visual and Inertial Odometry

This project encompassed the design of a mobile, real-time localization device for use in an indoor environment. A system was designed and constructed using visual and inertial odometry methods to meet the project requirements. Stereoscopic image features were detected through a C++ Sobel filter implementation and matched. An inertial measurement unit (IMU) provided raw acceleration and rotation coordinates which were transformed into a global frame of reference. A Kalman filter produced motion approximations from the input data and transmitted the Kalman position state coordinates via a radio transceiver to a remote base station. This station used a graphical user interface to map the incoming coordinates

Autonomous Quadrotor Navigation by Detecting Vanishing Points in Indoor Environments

abstract: Toward the ambitious long-term goal of a fleet of cooperating Flexible Autonomous Machines operating in an uncertain Environment (FAME), this thesis addresses various perception and control problems in autonomous aerial robotics. The objective of this thesis is to motivate the use of perspective cues in single images for the planning and control of quadrotors in indoor environments. In addition to providing empirical evidence for the abundance of such cues in indoor environments, the usefulness of these perspective cues is demonstrated by designing a control algorithm for navigating a quadrotor in indoor corridors. An Extended Kalman Filter (EKF), implemented on top of the vision algorithm, serves to improve the robustness of the algorithm to changing illumination. In this thesis, vanishing points are the perspective cues used to control and navigate a quadrotor in an indoor corridor. Indoor corridors are an abundant source of parallel lines. As a consequence of perspective projection, parallel lines in the real world, that are not parallel to the plane of the camera, intersect at a point in the image. This point is called the vanishing point of the image. The vanishing point is sensitive to the lateral motion of the camera and hence the quadrotor. By tracking the position of the vanishing point in every image frame, the quadrotor can navigate along the center of the corridor. Experiments are conducted using the Augmented Reality (AR) Drone 2.0. The drone is equipped with the following componenets: (1) 720p forward facing camera for vanishing point detection, (2) 240p downward facing camera, (3) Inertial Measurement Unit (IMU) for attitude control , (4) Ultrasonic sensor for estimating altitude, (5) On-board 1 GHz Processor for processing low level commands. The reliability of the vision algorithm is presented by flying the drone in indoor corridors.Dissertation/ThesisMasters Thesis Electrical Engineering 201

A Bioinspired Neural Model Based Extended Kalman Filter for Robot SLAM

Robot simultaneous localization and mapping (SLAM) problem is a very important and challenging issue in the robotic field. The main tasks of SLAM include how to reduce the localization error and the estimated error of the landmarks and improve the robustness and accuracy of the algorithms. The extended Kalman filter (EKF) based method is one of the most popular methods for SLAM. However, the accuracy of the EKF based SLAM algorithm will be reduced when the noise model is inaccurate. To solve this problem, a novel bioinspired neural model based SLAM approach is proposed in this paper. In the proposed approach, an adaptive EKF based SLAM structure is proposed, and a bioinspired neural model is used to adjust the weights of system noise and observation noise adaptively, which can guarantee the stability of the filter and the accuracy of the SLAM algorithm. The proposed approach can deal with the SLAM problem in various situations, for example, the noise is in abnormal conditions. Finally, some simulation experiments are carried out to validate and demonstrate the efficiency of the proposed approach

A scalable, portable, FPGA-based implementation of the Unscented Kalman Filter

Sustained technological progress has come to a point where robotic/autonomous systems may well soon become ubiquitous. In order for these systems to actually be useful, an increase in autonomous capability is necessary for aerospace, as well as other, applications. Greater aerospace autonomous capability means there is a need for high performance state estimation. However, the desire to reduce costs through simplified development processes and compact form factors can limit performance. A hardware-based approach, such as using a Field Programmable Gate Array (FPGA), is common when high performance is required, but hardware approaches tend to have a more complicated development process when compared to traditional software approaches; greater development complexity, in turn, results in higher costs. Leveraging the advantages of both hardware-based and software-based approaches, a hardware/software (HW/SW) codesign of the Unscented Kalman Filter (UKF), based on an FPGA, is presented. The UKF is split into an application-specific part, implemented in software to retain portability, and a non-application-specific part, implemented in hardware as a parameterisable IP core to increase performance. The codesign is split into three versions (Serial, Parallel and Pipeline) to provide flexibility when choosing the balance between resources and performance, allowing system designers to simplify the development process. Simulation results demonstrating two possible implementations of the design, a nanosatellite application and a Simultaneous Localisation and Mapping (SLAM) application, are presented. These results validate the performance of the HW/SW UKF and demonstrate its portability, particularly in small aerospace systems. Implementation (synthesis, timing, power) details for a variety of situations are presented and analysed to demonstrate how the HW/SW codesign can be scaled for any application

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

The development of localisation capabilities and control for a low-cost robot

Includes bibliographical references (leaves 58-61).A fully autonomous robot which can perform dangerous or mundane tasks is the ideal outcome of robotic research. A variety of commercially available household robots such as robotic vacuum cleaners exist but are limited in their navigation ability. In general, they tend to use random search patterns to navigate a room and overestimate the time required to clean the room in order to ensure covering the entire area. The ability to map the environment and then use this map to navigate is an essential step towards total autonomy, and would greatly improve the efficiency of these household robots. Autonomous mapping is a complex problem as the robot must use sensor readings to generate a map while at the same time using that map to locate itself and navigate. One component of the mapping task is localisation. This is the process of determining position and orientation from sensor data given a known map. This was the focus of this work as a first-step towards an autonomous mapping robot. This project continued the work of an undergraduate thesis in which a robot vacuum base was built. Using this base, the sensing and control systems were developed. The selection of a suitable controller was an important aspect of the development. It had to be suitable not only for this task but allow for expansion of the control capabilities should the project be extended. The Gumstix/Roboaudiostix embedded system was chosen and performed successfully. Its extremely small size and low power requirements are a feature of the system

Ultrasonic sensor platforms for non-destructive evaluation

Robotic vehicles are receiving increasing attention for use in Non-Destructive Evaluation (NDE), due to their attractiveness in terms of cost, safety and their accessibility to areas where manual inspection is not practical. A reconfigurable Lamb wave scanner, using autonomous robotic platforms is presented. The scanner is built from a fleet of wireless miniature robotic vehicles, each with a non-contact ultrasonic payload capable of generating the A0 Lamb wave mode in plate specimens. An embedded Kalman filter gives the robots a positional accuracy of 10mm. A computer simulator, to facilitate the design and assessment of the reconfigurable scanner, is also presented. Transducer behaviour has been simulated using a Linear Systems approximation (LS), with wave propagation in the structure modelled using the Local Interaction Simulation Approach (LISA). Integration of the LS and LISA approaches were validated for use in Lamb wave scanning by comparison with both analytical techniques and more computationally intensive commercial finite element/diference codes. Starting with fundamental dispersion data, the work goes on to describe the simulation of wave propagation and the subsequent interaction with artificial defects and plate boundaries. The computer simulator was used to evaluate several imaging techniques, including local inspection of the area under the robot and an extended method that emits an ultrasonic wave and listens for echos (B-Scan). These algorithms were implemented in the robotic platform and experimental results are presented. The Synthetic Aperture Focusing Technique (SAFT) was evaluated as a means of improving the fidelity of B-Scan data. It was found that a SAFT is only effective for transducers with reasonably wide beam divergence, necessitating small transducers with a width of approximately 5mm. Finally, an algorithm for robot localisation relative to plate sections was proposed and experimentally validated