Intervention AUV

About: Intervention AUV is a research topic. Over the lifetime, 980 publications have been published within this topic receiving 14130 citations.

Simultaneous mapping and planning for autonomous underwater vehicles in unknown environments

Abstract: New potential applications of autonomous underwater vehicles (AUVs) involve operations in unknown and cluttered environments, therefore increasing the vehicle exposure to collisions. To cope with these situations, we use an AUV framework for planning collision-free paths in unknown environments, which adapt and replan the paths according to nearby obstacles perceived during the mission execution using different range sensing sonar. We present simulation and real-world results for the SPARUS-II AUV, a torpedo-shaped vehicle, performing autonomous missions.

Acoustic real-time, low-power FPGA based obstacle detection for AUVs

Abstract: The underwater robots called Unmanned Underwater Vehicles (UUVs) take over complex and dangerous underwater missions that were previously performed by humans. These vehicles operate in the unknown environments and make their own decisions within the mission based on the readings of the sensors, without any link with a human operator. Independent of the mission, it is critical for the AUVs to be able to avoid submerged obstacles such as cliffs, wrecks, and floating mines. The AUV typically uses underwater imaging sonar that has several drawbacks for obstacle detection purposes, and therefore requires complex image processing algorithms. Due to the imaging sonar limitations, addressing obstacle detection using conventional software algorithms cannot meet an AUV's real-time, low power requirements. A low-power FPGA algorithm for underwater obstacle detection that is based on local image histogram entropy is proposed. The algorithm maintains a real-time reliable performance while meeting the AUV low power budget.

Advances in Sea Coverage Methods Using Autonomous Underwater Vehicles (AUVs)

Abstract: Due to rapid changes in modern technological development over the last several years, researches on small Autonomous Underwater Vehicles (AUVs) and Unmanned Underwater Vehicles (UUVs) appeared as important issues for various possibilities of application in the ocean. Military applications together with commercial need require practical details which are robust and cheap for realization as a product. In addition, works on coverage have been motivated by a wide range of real world applications that include non-humanitarian demining, deep-sea development sweeping and robotic spray-painting. Currently, many coverage applications utilize on-line coverage algorithms (Gabriely et al., 2003), where robots do not rely on a priori knowledge of a work-area, and thus must construct their motion trajectories step-by-step, addressing discovered obstacles as they move. This is the main contrast from the conventional off-line coverage algorithms, where robots are given a map of a work-area, and can therefore plan their paths ahead of deployment (Ge et al., 2005). In this paper, we focus on on-line coverage for underwater environment by multiple AUVs. Relevant works have shown that one of advantages of adopting multiple robots for a coverage task is the potential ability for more efficient coverage (Hazon et al, 2005). However, another advantage is that they usually offer greater robustness. Unfortunately,this important capability has been neglected in previous works done on on-line algorithms. As far as the details of the on-line coverage methods, there are many kinds of covering motions that can be realized either by back-and-forth motions in vast cells, or by following a general Voronoi diagram in narrow cells (Acar et al., 2001). Exact cellular decomposition can also be achieved through the use of the boustrophedon decomposition (Choset, 2001) as well as through Morse functions (Acar, 2002). The boustrophedon approach has been extended in (Choset, 2001) to the multi-robot domain. Such form of coverage requires the coverage to be executed in formations, which may be accomplished in a variety of ways (Ge et al., 2005). Spanning Tree Coverage algorithms (Gabriely, 2003) have also been proposed for online coverage of an unknown and gridded environment by a robot. For coverage with multi-robot teams, a frontier-based exploration technique has been used to guide robots towards the region between known and unknown areas on the map (Yamauchi et al., 1998). The Mark and Cover (MAC) algorithm has been proposed in (Wagner et al., 2000) with the proved convergence and the bounds on cover time of the algorithm. In (Yang & Luo, 2004), a neural network model has been used for exploration of a O pe n A cc es s D at ab as e w w w .in te hw eb .c om

A Hybrid Propulsion Method for Underwater Vehicle

Abstract: Nowadays, Underwater Vehicles are widely used in ocean environment monitoring, ocean resources exploration and military areas. Therefore, it is required for Underwater Vehicles to have longer work time, wider work space, and what’s more, it is also required for Underwater Vehicles to have the ability to move agilely and locate ccurately. Aiming at those problems, the paper proposes a new hybrid propulsion method for underwater vehicles. The hybrid propulsion which combines water jet ropulsion, vector propulsion and glide propulsion, is the adaptive underwater propulsion that has long-time and low-exhaust work continuation, and can accommodate some emergency. The paper analyzes contrastively the motion performance in adaptive work conditions and prospects its applications at last.

Product development at Kongsberg Maritime related to underwater sensor networks

Abstract: This paper presents selected product development activities in Kongsberg Maritime related to underwater sensor network nodes. Nodes include tethered fixed seabed nodes, easy deployable wireless nodes, catch monitoring for commercial fishery and Autonomous Underwater Vehicles (AUV). Sensors interfaced to the different nodes are touched upon, including scientific echo sounders. Further, the communication capabilities of the nodes are discussed with emphasis on wireless acoustic communication including PHY, MAC and network layer capabilities of the cNODE.