REMARO REMARO
Below, you can find links for the papers, that have been published up until now, from the ESRs inside the REMARO program!
A Collision Avoidance Method for Autonomous Underwater Vehicles Based on Long Short-Term Memories
Authors: László Antal, Martin Aubard, Erika Ábrahám, Ana Madureira, Luís Madureira, Maria Costa, José Pinto and Renato Campos
Date: March, 2023
Abstract
Over the past decades, underwater robotics has enjoyed growing popularity and relevance. While performing a mission, one crucial task for Autonomous Underwater Vehicles (AUVs) is bottom tracking, which should keep a constant distance from the seabed. Since static obstacles like walls, rocks, or shipwrecks can lie on the sea bottom, bottom tracking needs to be extended with obstacle avoidance. As AUVs face a wide range of uncertainties, implementing these essential operations is still challenging.
A simple rule-based control method has been proposed in [7] to realize obstacle avoidance. In this work, we propose an alternative AI-based control method using a Long Short-Term Memory network. We compare the performance of both methods using real-world data as well as via a simulator.
DOI: 10.1007/978-3-031-27499-2_42
Please see the link for the citation
Real-Time Automatic Wall Detection and Localization Based on Side Scan Sonar Images
Authors: Martin Aubard, Ana Madureira, Luis Madureira and José Pinto
Date: September, 2022
Abstract
Accurate identification of an uncertain underwater environment is one of the challenges of underwater robotics. Autonomous Underwater Vehicle (AUV) needs to understand its environment accurately to achieve autonomous tasks. The method proposed in this paper is a real-time automatic target recognition based on Side Scan Sonar images to detect and localize a harbor’s wall. This paper explains real-time Side Scan Sonar image generation and compares three object detection algorithms (YOLOv5, YOLOv5-TR, and YOLOX) using transfer learning. The YOLOv5-TR algorithm has the most accurate detection with 99% during training, whereas the YOLOX provides the best accuracy of 91.3% for a recorded survey detection. The YOLOX algorithm realizes the flow chart validation’s real-time detection and target localization.
DOI: 10.1109/AUV53081.2022.9965813
Please see the link for the citation
Belief-Based Fault Recovery for Marine Robotics
Authors: Jeremy Paul Coffelt, Mahya Mohammadi Kashani, Andrzej Wąsowski and Peter Kampmann
Date: August, 2022
Abstract
We propose a framework expanding the capabilities of underwater robots to autonomously recover from anomalous situations. The framework is built around a knowledge model developed in three stages. First, we create a deterministic knowledge base to describe the “health” of hardware, software, and environment components involved in a mission. Next, we describe the same components probabilistically, defining probabilities of failures, faults, and fixes. Finally, we combine the deterministic and probabilistic knowledge into a minimal ROS package designed to detect failures, isolate the underlying faults, propose fixes for the faults, and determine which is the most likely to help. We motivate the solution with a camera fault scenario and demonstrate it with a thruster failure on a real AUV and a simulated ROV.
Please see the link for the citation
A Formal Model of Metacontrol in Maude
Authors: Juliane Päßler, Esther Aguadol, Gustavo Rezende Silva, Carlos Hernández Corbato, Silvia Lizeth Tapia Tarifa, Einar Broch Johnsen
Date: August, 2022
Abstract
Nowadays smart applications appear in domains spanning from commodity household applications to advanced underwater robotics. These smart applications require adaptation to dynamic environments, changing requirements and internal system errors Metacontrol takes a systems of systems view on autonomous control systems and self-adaptation, by means of an additional layer of control that manipulates and combines the regular controllers. This paper develops a formal model of a Metacontrol architecture. We formalise this Metacontrol architecture in the context of an autonomous house heating application, enabling different controllers to be dynamically combined in order to meet user requirements to a better extent than the individual controllers in isolation. The formal model is developed in the Maude rewriting system, where we show results comparing different scenarios.
DOI: 10.1007/978-3-031-19849-6_32
Please see the link for the citation
A Deep Learning Framework for Semantic Segmentation of Underwater Environments
Authors: Amos Smith, Jeremy Coffelt and Kai Lingemann
Date: August, 2022
Abstract
Perception tasks such as object classification and segmentation are crucial to the operation of underwater robotics missions like bathymetric surveys and infrastructure inspections. Marine robots in these applications typically use a combination of laser scanner, camera, and sonar sensors to generate images and point clouds of the environment. Traditional perception approaches often struggle to overcome water turbidity, light attenuation, marine snow, and other harsh conditions of the underwater world. Deep learning-based perception techniques have proven capable of overcoming such difficulties, but are often limited by the availability of relevant training data. In this paper, we propose a framework that consists of procedural creation of randomized underwater pipeline environment scenes, the generation of corresponding point clouds with semantic labels, and the training of a 3D segmentation network using the synthetic data. The resulting segmentation network is analyzed on real underwater point cloud data and compared with a traditional baseline approach.
DOI: 10.1109/OCEANS47191.2022.9977212
Please see the link for the citation
Model-Based Testing for System-Level Safety of Autonomous Underwater Robots
Authors: Sergio Quijano, Mahsa Varshosaz
Date: June, 2022
Abstract
For the deployment of autonomous robotic systems in mission- and safety-critical underwater environments, aspects such as reasoning and planning need to be designed to operate in highly dynamic, uncertain environments while assuring a safe and reliable operation. Systems are often deployed without a prior safety assessment or developed with safety analysis as a separate engineering process. In this paper, to tackle these challenges, we propose an initial research vision and plan with the envisioned contributions towards designing an approach for system-wide modeling and Model-Based Testing to support safety assessments of autonomous underwater robots.