The Dynamic Compact Control Language version 3

Schneider, Toby; Petillo, Stephanie; Schmidt, Henrik; Murphy, Christopher

doi:10.1109/oceans-genova.2015.7271608

Cited by 10 publications

(3 citation statements)

References 3 publications

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“…The AUV, equipped with a chip scale atomic clock (Cash et al, 2018) that was time‐synchronized before launch using GPS, transmitted a status message encoded with the dynamic compact control language (DCCL) (Schneider et al, 2015) on a predefined time division multiple access (TDMA) schedule. This message included a compressed version of the most up‐to‐date navigation solution as computed by HydroMAN (

{x}_{(auv| earth)}^{hm}

), and other mission parameters.…”

Section: Integrated Communication and Navigation Networkmentioning

confidence: 99%

“…This enabled further compression of the acoustic datagrams, as the correction

\delta x

has a higher information entropy compared to the absolute position,

x

, which could reach

\pm

10 km (Schneider et al, 2015). The bounds of

\delta x

were limited to

\pm

800 m to represent expected worst‐case scenario deviations with sufficiently high resolution.…”

Section: Integrated Communication and Navigation Networkmentioning

confidence: 99%

“…The AUV, equipped with a chip scale atomic clock (Cash et al, 2018) that was time-synchronized before launch using GPS, transmitted a status message encoded with the dynamic compact control language (DCCL) (Schneider et al, 2015)…”

Section: Overviewmentioning

confidence: 99%

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A high‐resolution AUV navigation framework with integrated communication and tracking for under‐ice deployments

Randeni

Schneider

Bhatt

et al. 2022

Journal of Field Robotics

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We developed an environmentally adaptive under-ice navigation framework that was deployed in the Arctic Beaufort Sea during the United States Navy Ice Exercise in March 2020 (ICEX20). This navigation framework contained two subsystems developed from the ground up: (1) an on-board hydrodynamic model-aided navigation (HydroMAN) engine, and (2) an environmentally and acoustically adaptive integrated communication and navigation network (ICNN) that provided acoustic navigation aiding to the former. The HydroMAN synthesized measurements from an inertial navigation system (INS), ice-tracking Doppler velocity log (DVL), ICNN and pressure sensor into its self-calibrating vehicle flight dynamic model to compute the navigation solution. The ICNN system, which consisted of four ice buoys outfitted with acoustic modems, trilaterated the vehicle position using the one-way-traveltimes (OWTT) of acoustic datagrams transmitted by the autonomous underwater vehicle (AUV) and received by the ice buoy network. The ICNN digested salinity and temperature information to provide model-assisted real-time OWTT range conversion to deliver accurate acoustic navigation updates to the HydroMAN. To decouple the contributions from the HydroMAN and ICNN subsystems towards a stable navigation solution, this article evaluates them separately: (1) HydroMAN was compared against DVL bottom-track aided INS during pre-ICEX20 engineering trials where both systems provided similar accuracy; (2) ICNN was evaluated by conducting a static experiment in the Arctic where the ICNN navigation updates were compared against GPS with ICNN error within low tens of meters. The joint HydroMAN-ICNN framework was tested during ICEX20, which provided a nondiverging high-resolution navigation solution-with the majority of error below 15 m-that facilitated a successful AUV recovery through a small ice hole after an 11 km untethered run in the upper and mid-water column.

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