5,386 research outputs found
Improving the energy efficiency of autonomous underwater vehicles by learning to model disturbances
Energy efficiency is one of the main challenges for long-term autonomy of AUVs (Autonomous Underwater Vehicles). We propose a novel approach for improving the energy efficiency of AUV controllers based on the ability to learn which external disturbances can safely be ignored. The proposed learning approach uses adaptive oscillators that are able to learn online the frequency, amplitude and phase of zero-mean periodic external disturbances. Such disturbances occur naturally in open water due to waves, currents, and gravity, but also can be caused by the dynamics and hydrodynamics of the AUV itself. We formulate the theoretical basis of the approach, and demonstrate its abilities on a number of input signals. Further experimental evaluation is conducted using a dynamic model of the Girona 500 AUV in simulation on two important underwater scenarios: hovering and trajectory tracking. The proposed approach shows significant energy-saving capabilities while at the same time maintaining high controller gains. The approach is generic and applicable not only for AUV control, but also for other type of control where periodic disturbances exist and could be accounted for by the controller. © 2013 IEEE
An Autonomous Surface Vehicle for Long Term Operations
Environmental monitoring of marine environments presents several challenges:
the harshness of the environment, the often remote location, and most
importantly, the vast area it covers. Manual operations are time consuming,
often dangerous, and labor intensive. Operations from oceanographic vessels are
costly and limited to open seas and generally deeper bodies of water. In
addition, with lake, river, and ocean shoreline being a finite resource,
waterfront property presents an ever increasing valued commodity, requiring
exploration and continued monitoring of remote waterways. In order to
efficiently explore and monitor currently known marine environments as well as
reach and explore remote areas of interest, we present a design of an
autonomous surface vehicle (ASV) with the power to cover large areas, the
payload capacity to carry sufficient power and sensor equipment, and enough
fuel to remain on task for extended periods. An analysis of the design and a
discussion on lessons learned during deployments is presented in this paper.Comment: In proceedings of MTS/IEEE OCEANS, 2018, Charlesto
A Robust Model Predictive Control Approach for Autonomous Underwater Vehicles Operating in a Constrained workspace
This paper presents a novel Nonlinear Model Predictive Control (NMPC) scheme
for underwater robotic vehicles operating in a constrained workspace including
static obstacles. The purpose of the controller is to guide the vehicle towards
specific way points. Various limitations such as: obstacles, workspace
boundary, thruster saturation and predefined desired upper bound of the vehicle
velocity are captured as state and input constraints and are guaranteed during
the control design. The proposed scheme incorporates the full dynamics of the
vehicle in which the ocean currents are also involved. Hence, the control
inputs calculated by the proposed scheme are formulated in a way that the
vehicle will exploit the ocean currents, when these are in favor of the
way-point tracking mission which results in reduced energy consumption by the
thrusters. The performance of the proposed control strategy is experimentally
verified using a Degrees of Freedom (DoF) underwater robotic vehicle inside
a constrained test tank with obstacles.Comment: IEEE International Conference on Robotics and Automation (ICRA-2018),
Accepte
Active SLAM for autonomous underwater exploration
Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.Peer ReviewedPostprint (published version
Development of Modeling and Simulation Platform for Path-Planning and Control of Autonomous Underwater Vehicles in Three-Dimensional Spaces
Autonomous underwater vehicles (AUVs) operating in deep sea and littoral environments have diverse applications including marine biology exploration, ocean environment monitoring, search for plane crash sites, inspection of ship-hulls and pipelines, underwater oil rig maintenance, border patrol, etc. Achieving autonomy in underwater vehicles relies on a tight integration between modules of sensing, navigation, decision-making, path-planning, trajectory tracking, and low-level control. This system integration task benefits from testing the related algorithms and techniques in a simulated environment before implementation in a physical test bed. This thesis reports on the development of a modeling and simulation platform that supports the design and testing of path planning and control algorithms in a synthetic AUV, representing a simulated version of a physical AUV. The approach allows integration between path-planners and closed-loop controllers that enable the synthetic AUV to track dynamically feasible trajectories in three-dimensional spaces. The dynamical behavior of the AUV is modeled using the equations of motion that incorporate the effects of external forces (e.g., buoyancy, gravity, hydrodynamic drag, centripetal force, Coriolis force, etc.), thrust forces, and inertial forces acting on the AUV. The equations of motion are translated into a state space formulation and the S-function feature of the Simulink and MATLAB scripts are used to evolve the state trajectories from initial conditions. A three-dimensional visualization of the resulting AUV motion is achieved by feeding the corresponding position and orientation states into an animation code. Experimental validation is carried out by performing integrated waypoint planner (e.g., using the popular A* algorithm) and PD controller implementations that allow the traversal of the synthetic AUV in two-dimensional (XY, XZ, YZ) and three-dimensional spaces. An underwater pipe-line inspection task carried out by the AUV is demonstrated in a simulated environment. The simulation testbed holds a potential to support planner and controller design for implementation in physical AUVs, thereby allowing exploration of various research topics in the field
A geometrical approach to the motion planning problem for a submerged rigid body
The main focus of this paper is the motion planning problem for a deeply submerged rigid body. The equations of motion are formulated and presented by use of the framework of differential geometry and these equations incorporate external dissipative and restoring forces. We consider a kinematic reduction of the affine connection control system for the rigid body submerged in an ideal fluid, and present an extension of this reduction to the forced affine connection control system for the rigid body submerged in a viscous fluid. The motion planning strategy is based on kinematic motions; the integral curves of rank one kinematic reductions. This method is of particular interest to autonomous underwater vehicles which can not directly control all six degrees of freedom (such as torpedo shaped AUVs) or in case of actuator failure (i.e., under-actuated scenario). A practical example is included to illustrate our technique
Intelligent Navigation for a Solar Powered Unmanned Underwater Vehicle
In this paper, an intelligent navigation system for
an unmanned underwater vehicle powered by renewable
energy and designed for shadow water inspection in
missions of a long duration is proposed. The system is
composed of an underwater vehicle, which tows a surface
vehicle. The surface vehicle is a small boat with
photovoltaic panels, a methanol fuel cell and
communication equipment, which provides energy and
communication to the underwater vehicle. The underwater
vehicle has sensors to monitor the underwater
environment such as sidescan sonar and a video camera in
a flexible configuration and sensors to measure the
physical and chemical parameters of water quality on
predefined paths for long distances. The underwater
vehicle implements a biologically inspired neural
architecture for autonomous intelligent navigation.
Navigation is carried out by integrating a kinematic
adaptive neuro‐controller for trajectory tracking and an
obstacle avoidance adaptive neuro‐ controller. The
autonomous underwater vehicle is capable of operating
during long periods of observation and monitoring. This
autonomous vehicle is a good tool for observing large areas
of sea, since it operates for long periods of time due to the
contribution of renewable energy. It correlates all sensor
data for time and geodetic position. This vehicle has been
used for monitoring the Mar Menor lagoon.Supported by the Coastal Monitoring
System for the Mar Menor (CMS‐ 463.01.08_CLUSTER)
project founded by the Regional Government of Murcia,
by the SICUVA project (Control and Navigation System
for AUV Oceanographic Monitoring Missions. REF:
15357/PI/10) founded by the Seneca Foundation of
Regional Government of Murcia and by the DIVISAMOS
project (Design of an Autonomous Underwater Vehicle
for Inspections and oceanographic mission‐UPCT: DPI‐
2009‐14744‐C03‐02) founded by the Spanish Ministry of
Science and Innovation from Spain
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