AUV SLAM and experiments using a mechanical scanning forward-looking sonar

Navigation technology is one of the most important challenges in the applications of autonomous underwater vehicles (AUVs) which navigate in the complex undersea environment. The ability of localizing a robot and accurately mapping its surroundings simultaneously, namely the simultaneous localization and mapping (SLAM) problem, is a key prerequisite of truly autonomous robots. In this paper, a modified-FastSLAM algorithm is proposed and used in the navigation for our C-Ranger research platform, an open-frame AUV. A mechanical scanning imaging sonar is chosen as the active sensor for the AUV. The modified-FastSLAM implements the update relying on the on-board sensors of C-Ranger. On the other hand, the algorithm employs the data association which combines the single particle maximum likelihood method with modified negative evidence method, and uses the rank-based resampling to overcome the particle depletion problem. In order to verify the feasibility of the proposed methods, both simulation experiments and sea trials for C-Ranger are conducted. The experimental results show the modified-FastSLAM employed for the navigation of the C-Ranger AUV is much more effective and accurate compared with the traditional methods

Monte Carlo Localization for an Autonomous Underwater Vehicle with a Low-Cost Sonar

This paper proposes a Monto Carlo based localization (MCL) algorithm for autonomous underwater vehicle (AUV) with a low-cost mechanical scanning imaging sonar (MSIS). As MSIS has a slow-sampling characteristic, its scan is distorted by the vehicle motion during the scan interval and the sonar readings are sparse. Our contribution is introducing this two-stage approach to overcome the shortages of MSIS to achieve accurate localization: 1) the scan formation module is devised to eliminate the motion induced distortion of sonar scan; 2) MCL is applied to estimate the AUV pose accurately by the Dead Reckoning (DR) result and the formed sonar scan. Results of simulation verify that the proposed algorithm performs well in terms of effectiveness and accuracy

Cost-effective robot for steep slope crops monitoring

This project aims to develop a low cost, simple and robust robot able to autonomously monitorcrops using simple sensors. It will be required do develop robotic sub-systems and integrate them with pre-selected mechanical components, electrical interfaces and robot systems (localization, navigation and perception) using ROS, for wine making regions and maize fields

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

Localization Algorithms for GNSS-denied and Challenging Environments

In this dissertation, the problem about localization in GNSS-denied and challenging environments is addressed. Specifically, the challenging environments discussed in this dissertation include two different types, environments including only low-resolution features and environments containing moving objects. To achieve accurate pose estimates, the errors are always bounded through matching observations from sensors with surrounding environments. These challenging environments, unfortunately, would bring troubles into matching related methods, such as fingerprint matching, and ICP. For instance, in environments with low-resolution features, the on-board sensor measurements could match to multiple positions on a map, which creates ambiguity; in environments with moving objects included, the accuracy of the estimated localization is affected by the moving objects when performing matching. In this dissertation, two sensor fusion based strategies are proposed to solve localization problems with respect to these two types of challenging environments, respectively. For environments with only low-resolution features, such as flying over sea or desert, a multi-agent localization algorithm using pairwise communication with ranging and magnetic anomaly measurements is proposed in this dissertation. A scalable framework is then presented to extend the multi-agent localization algorithm to be suitable for a large group of agents (e.g., 128 agents) through applying CI algorithm. The simulation results show that the proposed algorithm is able to deal with large group sizes, achieve 10 meters level localization performance with 180 km traveling distance, while under restrictive communication constraints. For environments including moving objects, lidar-inertial-based solutions are proposed and tested in this dissertation. Inspired by the CI algorithm presented above, a potential solution using multiple features motions estimate and tracking is analyzed. In order to improve the performance and effectiveness of the potential solution, a lidar-inertial based SLAM algorithm is then proposed. In this method, an efficient tightly-coupled iterated Kalman filter with a build-in dynamic object filter is designed as the front-end of the SLAM algorithm, and the factor graph strategy using a scan context technology as the loop closure detection is utilized as the back-end. The performance of the proposed lidar-inertial based SLAM algorithm is evaluated with several data sets collected in environments including moving objects, and compared with the state-of-the-art lidar-inertial based SLAM algorithms

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