Online compensation of heading sensor bias for low cost AUVs

Healey, A. J.; An, Edgar; Marco, D.B.

doi:10.1109/auv.1998.744437

Cited by 28 publications

(22 citation statements)

References 2 publications

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“…A β ∈ R n×n is a known asymptotically stable matrix, such that lim t→∞β (t) = 0. Defining the adaptive control law as in (10) and proceeding as in Section III, we obtain (17). The following result gives an adaptive control law that guarantees signal boundedness and asymptotic tracking.…”

Section: Mrac With Asymptotic Bias Estimationmentioning

confidence: 99%

Accommodating Sensor Bias in MRAC for State Tracking

Patre

Joshi

2011

AIAA Guidance, Navigation, and Control Conference

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The problem of accommodating unknown sensor bias is considered in a direct model reference adaptive control (MRAC) setting for state tracking using state feedback. Sensor faults can occur during operation, and if the biased state measurements are directly used with a standard MRAC control law, neither closed-loop signal boundedness, nor asymptotic tracking can be guaranteed and the resulting tracking errors may be unbounded or unacceptably large. A modified MRAC law is proposed, which combines a bias estimator with control gain adaptation, and it is shown that signal boundedness can be accomplished, although the tracking error may not go to zero. Further, for the case wherein an asymptotically stable sensor bias estimator is available, an MRAC control law is proposed to accomplish asymptotic tracking and signal boundedness. Such a sensor bias estimator can be designed if additional sensor measurements are available, as illustrated for the case wherein bias is present in the rate gyro and airspeed measurements. Numerical example results are presented to illustrate each of the schemes.

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Section: Mrac With Asymptotic Bias Estimationmentioning

confidence: 99%

Accommodating Sensor Bias in MRAC for State Tracking

Patre

Joshi

2011

AIAA Guidance, Navigation, and Control Conference

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“…The performance of DR navigation is directly proportional to the quality of the heading and velocity measurements [139], which each have their own inherent error sources. Heading measurements from magnetic compasses are often contaminated by random noise, heading dependent bias (deviation) and low bandwidth [53]. Fiber optic gyroscopes (FOGS) generate heading measurements that are much higher quality and bias free, but are currently only available from expensive inertial measurement units (IMUs) [98].…”

Section: Relative Positioningmentioning

confidence: 99%

Self consistent bathymetric mapping from robotic vehicles in the deep ocean

Roman¹

2005

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“…Most underwater navigation systems are based on Kalman Filters which merge Doppler velocity and inertial measurements (Larsen 2000). Corrections to the unbounded drift error inherent in such systems have been achieved by using the global positioning system (GPS) while on the surface (Healey et al 1998) or beacon-based long baseline (LBL) acoustic positioning systems (Whitcomb et al 1999). But frequently surfacing for GPS fixes may not be possible or desirable, and LBL beacons, which are typically used in long-duration open-water operations, must be deployed and surveyed before use.…”

Section: Introductionmentioning

confidence: 99%

Real‐Time SLAM with Octree Evidence Grids for Exploration in Underwater Tunnels

Fairfield

Kantor

Wettergreen

2007

Journal of Field Robotics

189

110

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At 110m in diameter and over 350m in depth, the cenote Zacatón in central Mexico is a unique flooded sinkhole.A platform for conducting preliminary sonar tests is tethered in place. AbstractWe describe a Simultaneous Localization and Mapping (SLAM) method for a hovering underwater vehicle that will explore underwater caves and tunnels, a true three dimensional (3D) environment. Our method consists of a Rao-Blackwellized particle filter with a 3D evidence grid map representation. We describe a procedure for dynamically adjusting the number of particles to provide real-time performance. We also describe how we adjust the particle filter prediction step to accommodate sensor degradation or failure. We present an efficient octree data structure which makes it feasible to maintain the hundreds of maps needed by the particle filter to accurately model large environments. This octree structure can exploit spatial locality and temporal shared ancestry between particles to reduce the processing and storage requirements. To test our SLAM method, we utilize data collected with manually-deployed sonar mapping vehicles in the Wakulla Springs cave system in Florida and the Sistema Zacatón in Mexico, as well as data collected by the DEPTHX vehicle in the test tank at the Austin Applied Research Laboratory. We demonstrate our mapping and localization approach with these realworld datasets.

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Online compensation of heading sensor bias for low cost AUVs

Cited by 28 publications

References 2 publications

Accommodating Sensor Bias in MRAC for State Tracking

Accommodating Sensor Bias in MRAC for State Tracking

Self consistent bathymetric mapping from robotic vehicles in the deep ocean

Real‐Time SLAM with Octree Evidence Grids for Exploration in Underwater Tunnels

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