Relative Pose Observability Analysis for 3D Nonholonomic Vehicles Based on Range Measurements Only

Parlangeli, Gianfranco; Pedone, Paola; Indiveri, Giovanni

doi:10.3182/20120919-3-it-2046.00031

Cited by 17 publications

(13 citation statements)

References 5 publications

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“…At last, notice that the described approach can be extended to the case of multi-vehicle navigation: in particular, relative localization can be achieved when a vehicle has access to the range from another one and they share knowledge of their inputs. Preliminary results relative to this problem have been addressed for different first order kinematics models in [8] [9] and [13].…”

Section: Discussionmentioning

confidence: 99%

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Observability analysis for single range localization

Arrichiello

Palma

Indiveri

et al. 2015

OCEANS 2015 - Genova

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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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Section: Discussionmentioning

confidence: 99%

“…On the other hand, the global observability properties of the considered nonlinear system can be studied building on a 'state augmentation' method that makes possible to deal with a linear time varying (LTV) representation of the system. Recent contributions relative to this approach are reported in [12] and [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Observability analysis for single range localization

Arrichiello

Palma

Indiveri

et al. 2015

OCEANS 2015 - Genova

Self Cite

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“…This line of reasoning is more general than the proof presented in Theorem and it can be invoked, more generally, when the method of state augmentation is applied as in this paper. Further discussion on this issue can be found in . Remark Before concluding this section, it is important to remark that, although the observability results were derived with respect to the nonlinear system , they also apply to the original nonlinear system as they are related through a Lyapunov transformation. Also, the design of an observer for the original nonlinear system follows simply by reversing the state transformation for the corresponding states of the augmented system, as it will be detailed in the following section.…”

Section: Filter Designmentioning

confidence: 99%

“…This line of reasoning is more general than the proof presented in Theorem 2 and it can be invoked, more generally, when the method of state augmentation is applied as in this paper. Further discussion on this issue can be found in [23].…”

Section: Observability Analysismentioning

confidence: 99%

“…Position and linear velocity global asymptotic stability (GAS) filters based on an ultra-short baseline (USBL) positioning system were presented by the authors in [13], while the extended Kalman filter (EKF) is the workhorse of the solution presented in [14]. Single range navigation solutions can be found in [15][16][17][18][19][20][21][22][23][24]. For interesting discussions and more detailed surveys on underwater vehicle navigation and sensing devices see [25,26].…”

Section: Introductionmentioning

confidence: 99%

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Sensor‐Based Long Baseline Navigation: Observability Analysis and Filter Design

Batista¹,

Silvestre²,

Oliveira³

2013

Asian Journal of Control

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This paper presents a novel long baseline (LBL) position and velocity navigation filter for underwater vehicles based directly on the sensor measurements. The solution departs from previous approaches as the range measurements are explicitly embedded in the filter design, therefore avoiding inversion algorithms and allowing also the consideration of the cases of reduced numbers of readings, in particular when there are only two or three distance measurements. The nonlinear system dynamics are considered to their full extent and no linearizations are carried out whatsoever. The filter error dynamics are globally exponentially stable (GES) and it is shown, in a realistic simulation environment, that the filter achieves similar performance to the extended Kalman filter (EKF) and outperforms linear position and velocity filters based on algebraic estimates of the position obtained from the range measurements.

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