487 research outputs found
Safe Maneuvering Near Offshore Installations: A New Algorithmic Tool
Maneuvers of human-operated and autonomous marine vessels in the safety zone of drilling rigs, wind farms and other installations present a risk of collision. This article proposes an algorithmic toolkit that ensures maneuver safety, taking into account the restrictions imposed by ship dynamics. The algorithms can be used for anomaly detection, decision making by a human operator or an unmanned vehicle guidance system. We also consider a response to failures in the vessel's control systems and emergency escape maneuvers. Data used by the algorithms come from the vessel's dynamic positioning control system and positional survey charts of the marine installations
Efficient Trajectory Planning and Control for USV with Vessel Dynamics and Differential Flatness
Unmanned surface vessels (USVs) are widely used in ocean exploration and
environmental protection fields. To ensure that USV can successfully perform
its mission, trajectory planning and motion tracking are the two most critical
technologies. In this paper, we propose a novel trajectory generation and
tracking method for USV based on optimization theory. Specifically, the USV
dynamic model is described with differential flatness, so that the trajectory
can be generated by dynamic RRT* in a linear invariant system expression form
under the objective of optimal boundary value. To reduce the sample number and
improve efficiency, we adjust the trajectory through local optimization. The
dynamic constraints are considered in the optimization process so that the
generated trajectory conforms to the kinematic characteristics of the
under-actuated hull, and makes it easier to be tracked. Finally, motion
tracking is added with model predictive control under a sequential quadratic
programming problem. Experimental results show the planned trajectory is more
in line with the kinematic characteristics of USV, and the tracking accuracy
remains a higher level
Adaptive low-level control of autonomous underwater vehicles using deep reinforcement learning
Low-level control of autonomous underwater vehicles (AUVs) has been extensively addressed by classical control techniques. However, the variable operating conditions and hostile environments faced by AUVs have driven researchers towards the formulation of adaptive control approaches. The reinforcement learning (RL) paradigm is a powerful framework which has been applied in different formulations of adaptive control strategies for AUVs. However, the limitations of RL approaches have lead towards the emergence of deep reinforcement learning which has become an attractive and promising framework for developing real adaptive control strategies to solve complex control problems for autonomous systems. However, most of the existing applications of deep RL use video images to train the decision making artificial agent but obtaining camera images only for an AUV control purpose could be costly in terms of energy consumption. Moreover, the rewards are not easily obtained directly from the video frames. In this work we develop a deep RL framework for adaptive control applications of AUVs based on an actor-critic goal-oriented deep RL architecture, which takes the available raw sensory information as input and as output the continuous control actions which are the low-level commands for the AUV's thrusters. Experiments on a real AUV demonstrate the applicability of the stated deep RL approach for an autonomous robot control problem.Fil: Carlucho, Ignacio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; ArgentinaFil: de Paula, Mariano. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; ArgentinaFil: Wang, Sen. Heriot-Watt University; Reino UnidoFil: Petillot, Yvan. Heriot-Watt University; Reino UnidoFil: Acosta, Gerardo Gabriel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentin
A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning
Machine Learning (ML) techniques have gained significant traction as a means
of improving the autonomy of marine vehicles over the last few years. This
article surveys the recent ML approaches utilised for ship collision avoidance
(COLAV) and mission planning. Following an overview of the ever-expanding ML
exploitation for maritime vehicles, key topics in the mission planning of ships
are outlined. Notable papers with direct and indirect applications to the COLAV
subject are technically reviewed and compared. Critiques, challenges, and
future directions are also identified. The outcome clearly demonstrates the
thriving research in this field, even though commercial marine ships
incorporating machine intelligence able to perform autonomously under all
operating conditions are still a long way off
A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning
Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off.Peer reviewe
Toward Wheeled Mobility on Vertically Challenging Terrain: Platforms, Datasets, and Algorithms
Most conventional wheeled robots can only move in flat environments and
simply divide their planar workspaces into free spaces and obstacles. Deeming
obstacles as non-traversable significantly limits wheeled robots' mobility in
real-world, extremely rugged, off-road environments, where part of the terrain
(e.g., irregular boulders and fallen trees) will be treated as non-traversable
obstacles. To improve wheeled mobility in those environments with vertically
challenging terrain, we present two wheeled platforms with little hardware
modification compared to conventional wheeled robots; we collect datasets of
our wheeled robots crawling over previously non-traversable, vertically
challenging terrain to facilitate data-driven mobility; we also present
algorithms and their experimental results to show that conventional wheeled
robots have previously unrealized potential of moving through vertically
challenging terrain. We make our platforms, datasets, and algorithms publicly
available to facilitate future research on wheeled mobility.Comment: https://www.youtube.com/watch?v=uk62ITBGoTI
https://cs.gmu.edu/~xiao/Research/Verti-Wheelers
A multirobot platform based on autonomous surface and underwater vehicles with bio-inspired neurocontrollers for long-term oil spills monitoring
This paper describes the BUSCAMOS-Oil monitoring system, which is a robotic platform consisting of an autonomous surface vessel combined with an underwater vehicle. The system has been designed for the long-term monitoring of oil spills, including the search for the spill, and transmitting information on its location, extent, direction and speed. Both vehicles are controlled by two different types of bio-inspired neural networks: a Self-Organization Direction Mapping Network for trajectory generation and a Neural Network for Avoidance Behaviour for avoiding obstacles. The systems’ resilient capabilities are provided by bio-inspired algorithms implemented in a modular software architecture and controlled by redundant devices to give the necessary robustness to operate in the difficult conditions typically found in long-term oil-spill operations. The efficacy of the vehicles’ adaptive navigation system and long-term mission capabilities are shown in the experimental results.This work was partially supported by the BUSCAMOS Project (ref. 1003211003700) under the program DN8644 COINCIDENTE of the Spanish Defense Ministry, the “Research Programme for Groups of Scientific Excellence at Region of Murcia” of the Seneca Foundation (Agency for Science and Technology of the Region of Murcia-19895/GERM/15)”, and the Spanish Government’s cDrone (ref. TIN2013-45920-R) and ViSelTR (ref. TIN2012-39279) projects
Learning to Model and Plan for Wheeled Mobility on Vertically Challenging Terrain
Most autonomous navigation systems assume wheeled robots are rigid bodies and
their 2D planar workspaces can be divided into free spaces and obstacles.
However, recent wheeled mobility research, showing that wheeled platforms have
the potential of moving over vertically challenging terrain (e.g., rocky
outcroppings, rugged boulders, and fallen tree trunks), invalidate both
assumptions. Navigating off-road vehicle chassis with long suspension travel
and low tire pressure in places where the boundary between obstacles and free
spaces is blurry requires precise 3D modeling of the interaction between the
chassis and the terrain, which is complicated by suspension and tire
deformation, varying tire-terrain friction, vehicle weight distribution and
momentum, etc. In this paper, we present a learning approach to model wheeled
mobility, i.e., in terms of vehicle-terrain forward dynamics, and plan
feasible, stable, and efficient motion to drive over vertically challenging
terrain without rolling over or getting stuck. We present physical experiments
on two wheeled robots and show that planning using our learned model can
achieve up to 60% improvement in navigation success rate and 46% reduction in
unstable chassis roll and pitch angles.Comment: https://www.youtube.com/watch?v=VzpRoEZeyWk
https://cs.gmu.edu/~xiao/Research/Verti-Wheelers
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