Emili Hernández

Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar

This paper proposes a pose-based algorithm to solve the full SLAM problem for an Autonomous Underwater Vehicle (AUV), navigating in an unknown and possibly unstructured environment. The technique incorporate probabilistic scan matching with range scans gathered from a Mechanical Scanning Imaging Sonar (MSIS) and the robot dead-reckoning displacements estimated from a Doppler Velocity Log (DVL) and a Motion Reference Unit (MRU). The proposed method utilizes two Extended Kalman Filters (EKF). The first, estimates the local path travelled by the robot while grabbing the scan as well as its uncertainty and provides position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augment state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. The algorithm has been tested on an AUV guided along a 600m path within a marina environment, showing the viability of the proposed approach.

Mission control system for dam inspection with an AUV

This paper presents a complete control architecture that has been designed to fulfill predefined missions with an autonomous underwater vehicle (AUV). The control architecture has three levels of control: mission level, task level and vehicle level. The novelty of the work resides in the mission level, which is built with a Petri network that defines the sequence of tasks that are executed depending on the unpredictable situations that may occur. The task control system is composed of a set of active behaviours and a coordinator that selects the most appropriate vehicle action at each moment. The paper focuses on the design of the mission controller and its interaction with the task controller. Simulations, inspired on an industrial underwater inspection of a dam grate, show the effectiveness of the control architecture

Navigating and mapping with the SPARUS AUV in a natural and unstructured underwater environment

In spite of the recent advances in unmanned underwater vehicles (UUV) navigation techniques, robustly solving their localization in unstructured and unconstrained areas is still a challenging problem. In this paper, we propose a pose-based algorithm to solve the full Simultaneous Localization And Mapping (SLAM) problem for an Autonomous Underwater Vehicle (AUV), navigating in the unknown and unstructured environment. A probabilistic scan matching technique using range scans gathered from a Mechanical Scanning Imaging Sonar (MSIS) is used together with the robot dead-reckoning displacements. The raw data from the sensors are processed and fused in-line with an augmented state extended Kalman filter (EKF), that estimates and keeps the scans poses. The proposed SLAM method has been tested with a real world dataset acquired from the Sparus AUV, guided in a natural underwater environment.

A topologically guided path planner for an AUV using homotopy classes

The paper proposes a method that uses topological information to guide the path search in any 2D workspace. As a first contribution, we have extended the method proposed by Jenkins to generate the topological information by taking into consideration the constraints of the workspace during the construction of the topological environment, which is then used to compute topological paths. As a second contribution, a planner based on the Rapidly-exploring Random Tree (RRT), called Homotopic RRT (HRRT) is proposed to use the topological information to guide the path search in the workspace. Finally, simulated and real results with an Autonomous Underwater Vehicle (AUV) are presented showing the feasibility of the proposal. Comparison with well-known path planning algorithms has also been included.

ICTINEUAUV Wins the First SAUC-E Competition

A pioneer team of students of the University of Girona decided to design and develop an autonomous underwater vehicle (AUV) called ICTINEUAUV to face the Student Autonomous Underwater Challenge-Europe (SAUC-E). The prototype has evolved from the initial computer aided design (CAD) model to become an operative AUV in the short period of seven months. The open frame and modular design principles together with the compatibility with other robots previously developed at the lab have provided the main design philosophy. Hence, at the robots core, two networked computers give access to a wide set of sensors and actuators. The Gentoo/Linux distribution was chosen as the onboard operating system. A software architecture based on a set of distributed objects with soft real time capabilities was developed and a hybrid control architecture including mission control, a behavioural layer and a robust map-based localization algorithm made ICTINEUAUV the winning entry.

Underwater Telerobotics for Collaborative Research

Underwater robotics constitutes one of the most representative application fields of telerobotics. Over the last few decades, Remotely Operated Vehicles (ROVs) have played a very important role in undersea exploration/intervention, reducing the need of manned submersibles. In the future, Autonomous Underwater Vehicles (AUVs) equipped with acoustic modems and modern telerobotics technologies will do the job. This chapter explores underwater telerobotics reporting on the most representative scenarios involving ROVs and AUVs. A scenario involving the remote training of skilled researchers is analyzed in detail. In this context the tools needed for remote experimentation with the underwater robots developed by the University of Girona (Spain) are explained. These tools include a hardware in the loop (HIL) simulator called NEPTUNE and a control architecture called O 2 CA 2.

Probabilistic sonar scan matching SLAM for underwater environment

This paper proposes a pose-based algorithm to solve the full Simultaneous Localization And Mapping (SLAM) problem for an Autonomous Underwater Vehicle (AUV), navigating in an unknown and possibly unstructured environment. A probabilistic scan matching technique using range scans gathered from a Mechanical Scanning Imaging Sonar (MSIS) is used together with the robot dead-reckoning displacements. The proposed method utilizes two Extended Kalman Filters (EKFs). The first, estimates the local path traveled by the robot while forming the scan as well as its uncertainty, providing position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augmented state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. Also, a method of estimating the uncertainty of the scan matching estimation is provided. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment and is compared against previous work from the authors, showing the viability of the proposed approach.

Probabilistic sonar scan matching for an AUV

This paper proposes MSISpIC, a probabilistic sonar scan matching algorithm for the localization of an autonomous underwater vehicle (AUV). The technique uses range scans gathered with a Mechanical Scanning Imaging Sonar (MSIS), the robot displacement estimated through dead-reckoning using a Doppler velocity log (DVL) and a motion reference unit (MRU). The proposed method is an extension of the pIC algorithm. An extended Kalman filter (EKF) is used to estimate the robot-path during the scan in order to reference all the range and bearing measurements as well as their uncertainty to a scan fixed frame before registering. The major contribution consists of experimentally proving that probabilistic sonar scan matching techniques have the potential to improve the DVL-based navigation. The algorithm has been tested on an AUV guided along a 600 m path within an abandoned marina underwater environment with satisfactory results.

Occupancy Grid Mapping in an Underwater Structured Environment

Abstract This paper presents practical results about the occupancy grid mapping of an underwater man-made environment using a sensor suite commonly available in nowadays Autonomous Underwater Vehicles (AUVs). The proposed algorithms are tested to be incorporated as part of the design of a new motion control system to integrate reactive obstacle avoidance with local path planning techniques to provide safe real-time guidance capabilities. The paper focus on the use of a sonar scan matching improved dead-reckoning navigation (Doppler Velocity Log (DVL) and Motion Reference Unit (MRU) based) together with an standard occupancy grid mapping algorithm. A conventional inverse sensor model for a sonar profiler is used and compared against a new inverse sensor model proposed to take advantage of the use of widely available imaging sonars. The system is validated experimentally on a dataset gathered with an AUV guided along a 600 m path within a marina environment.

A comparison of homotopic path planning algorithms for robotic applications

This paper addresses the path planning problem for robotic applications using homotopy classes. These classes provide a topological description of how paths avoid obstacles, which is an added value to the path planning problem. Homotopy classes are generated and sorted according to a lower bound heuristic estimator using a method we developed. Then, the classes are used to constrain and guide path planning algorithms. Three different path planners are presented and compared: a graph-search algorithm called Homotopic A? (HA?), a probabilistic sample-based algorithm called Homotopic RRT (HRRT), and a bug-based algorithm called Homotopic Bug (HBug). Our method has been tested in simulation and in an underwater bathymetric map to compute the trajectory of an Autonomous Underwater Vehicle (AUV). A comparison with well-known path planning algorithms has also been included. Results show that our homotopic path planners improve the quality of the solutions of their respective non-homotopic versions with similar computation time while keeping the topological constraints. We present three path planners to generate solutions that follow homotopy classes.Homotopy classes provide an added value to the path planning problem.Our method generates paths with the topology of the optimal solution much faster.We show extensive results in synthetic scenarios and on a bathymetric map.