The issue of single range based observability analysis and observer design for the kinematics model of a 3D vehicle eventually subject to a constant unknown drift velocity is addressed. The proposed method departs from alternative solutions to the problem and leads to the definition of a linear time invariant state equation with a linear time varying output. Simple necessary and sufficient observability conditions are derived. The localization problem is finally solved using a novel outlier robust predictor -corrector state estimator. Numerical simulation examples are described to illustrate the performance of the method as compared to a standard Kalman filter.
The operation of a ROV requires significant off-shore dedicated manpower to handle and operate the robotic platform. In order to reduce the burden of operations, DexROV proposes to work out more cost effective and time efficient ROV operations, where manned support is in a large extent delocalized onshore (i.e. from a ROV control center), possibly at a large distance from the actual operations, relying on satellite communications. The proposed scheme makes provision for advanced dexterous manipulation capabilities, exploiting human expertise from a remote location when deemed useful. The outcomes of the project will be integrated and evaluated in a series of tests and evaluation campaigns, culminating with a realistic deep sea (1,300 meters) trial. After one year, the project specified the system architecture of the system and carried out preliminary technological trade-offs for the subsystems
In this paper, we present DexROV, a funded EC Horizon 2020 project that proposes to implement novel operation strategies for underwater semi-autonomous interventions. These costly and demanding operations are more and more often performed by ROVs (Remotely Operated Vehicles), contributing to risks cutting for human divers. However ROV operations require offshore structures, hosted on a support vessel with a crew of a significant amount of personnel necessary to properly handle and operate the robotic platform. One of the key goals of DexROV is to delocalize on-shore the manned support as much as possible, reducing the crew onboard the support vessel and consequently the whole operation costs and risks. The Control Center is located onshore, far away from the actual operation location. Operators interact with the ROV through a simulation environment that exploit 3D models of the environment built online relying on the perception and modeling capabilities of the robotic system and transmitted via satellite communication. Currently ROVs lack the dexterous capabilities needed to perform many kind of operations, for which human divers are still necessary. DexROV addresses this problem, equipping the ROV with two 6 DoF (Degrees of Freedom) dexterous manipulators with anthropomorphic end-effectorsand providing semi-autonomous capabilities. The control will rely on a multi-task priority approach that will help the operator to focus on the main operation, leaving the low-level tasks to be autonomously performed by the ROV.
This work addresses state estimation in presence of outliers in observed data. Outlying data and measurements have a most relevant impact in many control and signal processing applications including marine systems related ones: underwater navigation systems exploiting acoustic data, for example, are frequently affected by outlying measurements. Other on-board sensors and devices are likely to produce measurements contaminated by outlier because of the harsh operating conditions of marine systems. Given the general interest for dealing with measurement outliers in a number of applications, this paper describes a state estimation solution exhibiting robustness to output outliers. The system model is assumed to be linear (either time varying or time invariant) discrete time. The proposed observer is designed by extending an outlier robust static parameter identification algorithm to the case of a linear dynamic plant. The designed estimator has a predictor/corrector structure like the Kalman filter and the Luenberger observer. Simulation and experimental results are provided illustrating the robustness of the derived solution to measurement outliers as compared with the Kalman filter. The proposed solution is also compared with alternative outlier robust state estimation filters showing its effectiveness, in particular, in the presence of measurements outliers occurring in a consecutive sequence. Because of its deterministic execution time and limited numerical complexity, the proposed state estimator can be readily applied in real-time applications. Figure 4. Noisy output with outliers (depicted as blue stars, D 0:2) and non-outliers in grey generated through the model in (41). [Colour figure can be viewed at wileyonlinelibrary.com] The LEL Filter column refers to the interval of singular values over 3000 Monte Carlo runs assuming outliers generated through the model in (41). LEL, least entropy like.with synthetic and real data have been discussed showing the potentiality of the proposed filter as compared with the standard KF. Comparison with alternative robust filtering approaches have shown its robustness to sequences of successive measurement outliers as well. The proposed robust LEL filter may be exploited in real-time applications because of its limited and predictable (i.e. deterministic) computational effort.
The Widely scalable Mobile Underwater Sonar Technology (WiMUST) project is an H2020 Research and Innovation Action funded by the European Commission. The project aims at developing a system of cooperative autonomous underwater vehicles (AUVs) for geotechnical surveying and geophysical exploration. The paper describes the main objectives of the project, gives an overview of the methodologies adopted to achieve them, and summarizes the work done in the first year of R&D work.
This paper presents an observability analysis for the single range localization problem of a second order kinematics model of an Autonomous Underwater Vehicle (AUV) possibly subject to a constant current. In particular, the AUV is modeled as a double integrator having as input the acceleration in an inertial reference frame and as output its distance to a stationary beacon. Since the range is a non linear function of the position, the single range observability problem is inherently nonlinear. Thus, to eventually design an observer, we assess observability conditions addressing two complementary issues: local weak observability of the nonlinear system and global observability referring to a linear time varying representation of the system derived through a 'state augmentation' method. The proposed methods for observability analysis is discussed in different case studies (e.g. 2D/3D, absence/presence of current) and the performances of the related state observers are illustrated throughout numerical simulations.
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