Autonomous Visual Servo Robotic Capture of Non-cooperative Target

This doctoral research develops and validates experimentally a vision-based control scheme for the autonomous capture of a non-cooperative target by robotic manipulators for active space debris removal and on-orbit servicing. It is focused on the final capture stage by robotic manipulators after the orbital rendezvous and proximity maneuver being completed. Two challenges have been identified and investigated in this stage: the dynamic estimation of the non-cooperative target and the autonomous visual servo robotic control. First, an integrated algorithm of photogrammetry and extended Kalman filter is proposed for the dynamic estimation of the non-cooperative target because it is unknown in advance. To improve the stability and precision of the algorithm, the extended Kalman filter is enhanced by dynamically correcting the distribution of the process noise of the filter. Second, the concept of incremental kinematic control is proposed to avoid the multiple solutions in solving the inverse kinematics of robotic manipulators. The proposed target motion estimation and visual servo control algorithms are validated experimentally by a custom built visual servo manipulator-target system. Electronic hardware for the robotic manipulator and computer software for the visual servo are custom designed and developed. The experimental results demonstrate the effectiveness and advantages of the proposed vision-based robotic control for the autonomous capture of a non-cooperative target. Furthermore, a preliminary study is conducted for future extension of the robotic control with consideration of flexible joints

CES-515 Towards Localization and Mapping of Autonomous Underwater Vehicles: A Survey

Autonomous Underwater Vehicles (AUVs) have been used for a huge number of tasks ranging from commercial, military and research areas etc, while the fundamental function of a successful AUV is its localization and mapping ability. This report aims to review the relevant elements of localization and mapping for AUVs. First, a brief introduction of the concept and the historical development of AUVs is given; then a relatively detailed description of the sensor system used for AUV navigation is provided. As the main part of the report, a comprehensive investigation of the simultaneous localization and mapping (SLAM) for AUVs are conducted, including its application examples. Finally a brief conclusion is summarized

Probablistic approaches for intelligent AUV localisation

This thesis studies the problem of intelligent localisation for an autonomous underwater vehicle (AUV). After an introduction about robot localisation and specific issues in the underwater domain, the thesis will focus on passive techniques for AUV localisation, highlighting experimental results and comparison among different techniques. Then, it will develop active techniques, which require intelligent decisions about the steps to undertake in order for the AUV to localise itself. The undertaken methodology consisted in three stages: theoretical analysis of the problem, tests with a simulation environment, integration in the robot architecture and field trials. The conclusions highlight applications and scenarios where the developed techniques have been successfully used or can be potentially used to enhance the results given by current techniques. The main contribution of this thesis is in the proposal of an active localisation module, which is able to determine the best set of action to be executed, in order to maximise the localisation results, in terms of time and efficiency

Towards autonomous localization and mapping of AUVs: a survey

Purpose The main purpose of this paper is to investigate two key elements of localization and mapping of Autonomous Underwater Vehicle (AUV), i.e. to overview various sensors and algorithms used for underwater localization and mapping, and to make suggestions for future research. Design/methodology/approach The authors first review various sensors and algorithms used for AUVs in the terms of basic working principle, characters, their advantages and disadvantages. The statistical analysis is carried out by studying 35 AUV platforms according to the application circumstances of sensors and algorithms. Findings As real-world applications have different requirements and specifications, it is necessary to select the most appropriate one by balancing various factors such as accuracy, cost, size, etc. Although highly accurate localization and mapping in an underwater environment is very difficult, more and more accurate and robust navigation solutions will be achieved with the development of both sensors and algorithms. Research limitations/implications This paper provides an overview of the state of art underwater localisation and mapping algorithms and systems. No experiments are conducted for verification. Practical implications The paper will give readers a clear guideline to find suitable underwater localisation and mapping algorithms and systems for their practical applications in hand. Social implications There is a wide range of audiences who will benefit from reading this comprehensive survey of autonomous localisation and mapping of UAVs. Originality/value The paper will provide useful information and suggestions to research students, engineers and scientists who work in the field of autonomous underwater vehicles

6-DoF Navigation Systems for Autonomous Underwater Vehicles

Austri

Vision-Based Autonomous Landing of a Quadrotor on the Perturbed Deck of an Unmanned Surface Vehicle

Autonomous landing on the deck of an unmanned surface vehicle (USV) is still a major challenge for unmanned aerial vehicles (UAVs). In this paper, a fiducial marker is located on the platform so as to facilitate the task since it is possible to retrieve its six-degrees of freedom relative-pose in an easy way. To compensate interruption in the marker’s observations, an extended Kalman filter (EKF) estimates the current USV’s position with reference to the last known position. Validation experiments have been performed in a simulated environment under various marine conditions. The results confirmed that the EKF provides estimates accurate enough to direct the UAV in proximity of the autonomous vessel such that the marker becomes visible again. Using only the odometry and the inertial measurements for the estimation, this method is found to be applicable even under adverse weather conditions in the absence of the global positioning system

Extended Kalman Filter Implementation for the Khepera II Mobile Robot

The accurate estimation of robot position and orientation in real-time is one of the fundamental challenges in mobile robotics. The Extended Kalman Filter is a nonlinear real-time recursive time domain filter that combines available sensor data to produce an accurate estimate of state, and has been successfully applied to the localization problem in mobile robotics and aircraft navigation. This report describes an Extended Kalman Filter implementa- tion for the Khepera II mobile robotics platform that seeks to produce accurate localization estimates in real-time using wheel odometry data, IR sensor range data, and compass heading data

Framework for Fast Experimental Testing of Autonomous Navigation Algorithms

Research in mobile robotics requires fully operative autonomous systems to test and compare algorithms in real-world conditions. However, the implementation of such systems remains to be a highly time-consuming process. In this work, we present an robot operating system (ROS)-based navigation framework that allows the generation of new autonomous navigation applications in a fast and simple way. Our framework provides a powerful basic structure based on abstraction levels that ease the implementation of minimal solutions with all the functionalities required to implement a whole autonomous system. This approach helps to keep the focus in any sub-problem of interest (i.g. localization or control) while permitting to carry out experimental tests in the context of a complete application. To show the validity of the proposed framework we implement an autonomous navigation system for a ground robot using a localization module that fuses global navigation satellite system (GNSS) positioning and Monte Carlo localization by means of a Kalman filter. Experimental tests are performed in two different outdoor environments, over more than twenty kilometers. All the developed software is available in a GitHub repository.This work has been supported by InterregV Sudoe and FEDER programs of European Commission through the COMMANDIA project SOE2/P1/F0638, and by the Spanish Government through the FPU grant FPU15/04446 and the research project RTI2018-094279-B-I00