Localisation, i.e. estimation of one’s position in a given environment is a crucial element of many mobile systems, manned and unmanned. Due to the high demand for autonomous exploration, patrolling and inspection services and a rapid improvement of batteries, sensors and machine learning algorithms, the quality of localisation becomes even more important for smart robotic systems. The underwater domain is a very challenging environment due to the water blocking most of the signals over short distances. Recent results in localisation techniques for underwater vehicles are summarised in two principal categories: passive techniques, which strive to provide the best estimation of the vehicle’s position (global or local) given the past and current information from sensors, and active techniques, which additionally produce guidance output that is expected to minimise the uncertainty of estimated position.

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, showing the viability of the proposed approach.

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 600 m path within a marina environment, showing the viability of the proposed approach.

This paper presents a sonar-based localization approach for an autonomous underwater vehicle, in structured and unstructured environments. The system is based on a particle filter approach to represent the vehicle state and it uses a mechanically scanned profiling sonar, acquiring range profiles. A modification to the standard particle filter algorithm is proposed, in order to explore the state space in a more effective way and to reduce computational complexity. The proposed system was validated both in simulation and in trials involving a real vehicle, showing a high robustness and real-time capabilities.

PANDORA is a three year project that is developing new computational methods to make underwater robots Persistently Autonomous, significantly reducing the frequency of assistance requests. The aim of the project is to extend the range of tasks that can be carried on autonomously and increase their complexity while reducing the need for operator assistances. Dynamic adaptation to the change of conditions is very important while addressing autonomy in the real world and not just in well-known situation. The key of Pandora is the ability to recognise failure and respond to it, at all levels of abstraction. Under the guidance of major industrial players, validation tasks of inspection, cleaning and valve turning will be trialled with partners’ AUVs in Scotland and Spain

The article describes observations from the online teaching of a robotics class during the COVID-19 pandemic caused by SARS-CoV-2, also known as the coronavirus. The changes in the course structure and in the provided material lead to an unexpected increase in the grade performance of the students. The article provides a description and an analysis of the effects and their possible causes. In addition to a grade-performance analysis, further data from a university-wide and from a course-specific survey are used. The analysis of the effects and their possible causes is furthermore discussed in relation to the educational research literature. Some evidence for the general findings is provided, which are of interest for online teaching or blended learning in general, respectively, for teaching in robotics and related areas. These include some evidence for the benefits of asynchronous online teaching and for the role of social interaction, which may happen in self-organized, smaller peer groups, even without the intervention of the instructor. The findings and the extensive pointers to the literature can also provide useful guidelines for instructors of robotics courses when considering the use of online or blended teaching in the future beyond the COVID-19 pandemic.