Understanding the power requirements of autonomous underwater systems, Part I: An analytical model for optimum swimming speeds and cost of transport

Phillips, Alexander B.; Haroutunian, Maryam; Murphy, Alan J.; Boyd, Stephen; Blake, J.I.R.; Griffiths, Gwyn

doi:10.1016/j.oceaneng.2015.12.014

Cited by 23 publications

(20 citation statements)

References 34 publications

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“…The average power consumption misses one key information for understanding mission performance: It does not take the distance covered into consideration. A normalised measure that considers the energy required to move a vehicle by a unit distance is the cost of transport (COT; Phillips et al, ).

normalC normalO normalT false(J / m \cdot k g false) = \frac{Energy false(normalJ false)}{Distance false(normalm false) \cdot Mass (kg)} .

Delphin2 has a mass of 79.40 kg, the energy available is estimated as

\begin{matrix} true0.33em \\ left E_{b a t t e r y} & left = & left Number of cells \cdot Nominal voltage \cdot Battery capacity \\ left & left = & left 3 \cdot 21.6 V \cdot 10 normalA normalh = 2333 normalk normalJ . \end{matrix}

…”

Section: Resultsmentioning

confidence: 99%

“…The total energy used during one experiment is calculated by integrating the product of the supply voltage and the current drawn The average power consumption misses one key information for understanding mission performance: It does not take the distance covered into consideration. A normalised measure that considers the energy required to move a vehicle by a unit distance is the cost of transport (COT; Phillips et al, 2017).…”

Section: Cost Of Transportmentioning

confidence: 99%

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Experimental analysis of low‐altitude terrain following for hover‐capable flight‐style autonomous underwater vehicles

Schillai

Turnock

Rogers

et al. 2019

Journal of Field Robotics

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Operating an autonomous underwater vehicle (AUV) in close proximity to terrain typically relies solely on the vehicle sensors for terrain detection, and challenges the manoeuvrability of energy efficient flight‐style AUVs. This paper gives new results on altitude tracking limits of such vehicles by using the fully understood environment of a lake to perform repeated experiments while varying the altitude demand, obstacle detection and actuator use of a hover‐capable flight‐style AUV. The results are analysed for mission success, vehicle risk and repeatability, demonstrating the terrain following capabilities of the overactuated AUV over a range of altitude tracking strategies and how these measures better inform vehicle operators. A major conclusion is that the effects of range limits, bias and false detections of the sensors used for altitude tracking must be fully accounted for to enable mission success. Furthermore it was found that switching between hover‐ and flight‐style actuations based on speed, whilst varying the operation speed, has advantages for performance improvement over combining hover‐ and flight‐style actuators at high speeds.

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normalC normalO normalT false(J / m \cdot k g false) = \frac{Energy false(normalJ false)}{Distance false(normalm false) \cdot Mass (kg)} .

Delphin2 has a mass of 79.40 kg, the energy available is estimated as

\begin{matrix} true0.33em \\ left E_{b a t t e r y} & left = & left Number of cells \cdot Nominal voltage \cdot Battery capacity \\ left & left = & left 3 \cdot 21.6 V \cdot 10 normalA normalh = 2333 normalk normalJ . \end{matrix}

…”

Section: Resultsmentioning

confidence: 99%

Section: Cost Of Transportmentioning

confidence: 99%

Experimental analysis of low‐altitude terrain following for hover‐capable flight‐style autonomous underwater vehicles

Schillai

Turnock

Rogers

et al. 2019

Journal of Field Robotics

View full text Add to dashboard Cite

show abstract

“…However, maximum payloads remain limited compared to surface vessels (e.g., <1 m 3 ), and increasing payload size increases propulsive power requirements and overall vehicle costs. Given the limits to propulsive efficiency (Phillips et al, 2017), there remains a significant gap between the payloads that can be supported by a manned or autonomous surface vessel and those that can operate autonomously underwater for extended periods. Similarly, "high" power consumption for sensors is on the order of 10 W (Hobson et al, 2012;Roper et al, 2017), which is still quite low in absolute terms compared to what is possible for cabled observations.…”

Section: Energy Sourcesmentioning

confidence: 99%

Future Vision for Autonomous Ocean Observations

Whitt¹,

Pearlman²,

Polagye

et al. 2020

Front. Mar. Sci.

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Whitt et al. Future of Autonomous Ocean Observations reductions. Cost reductions could enable order-of-magnitude increases in platform operations and increase sampling resolution for a given level of investment. Energy harvesting technologies should be integral to the system design, for sensors, platforms, vehicles, and docking stations. Connections are needed between the marine energy and ocean observing communities to coordinate among funding sources, researchers, and end users. Regional teams should work with global organizations such as IOC/GOOS in governance development. International networks such as emerging glider operations (EGO) should also provide a forum for addressing governance. Networks of multiple vehicles can improve operational efficiencies and transform operational patterns. There is a need to develop operational architectures at regional and global scales to provide a backbone for active networking of autonomous platforms.

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“…The power consumption, P tot , of a neutrally buoyant flight style AUV can be assumed to be split between hotel load, P H , which is invariant to forward speed, and propulsion power, P P , which is related to propulsion speed raised to the power 3 [40,41]. For a positively buoyant over-actuated vehicle, additional power is required to maintain depth, P B :…”

Section: Energy Consumptionmentioning

confidence: 99%

Depth control for an over-actuated, hover-capable autonomous underwater vehicle with experimental verification

et al. 2017

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A PI-D based control system is developed for over-actuated, hover-capable AUVs which enables a smooth transition from hover-style to flight-style operation. A system stability and convergence is proven using a Lyapunov-based approach. The performance of the controller is demonstrated by simulation but crucially is proven to provide satisfactory performance experimentally. The approach is able to operate over a range of vehicle ballasting configurations, and to imposed external disturbances. The proposed system is computationally inexpensive and does not require a detailed hydrodynamic model to implement. By monitoring the energy consumption on board, the cost of maintaining depth at a range of forward speeds with different buoyancy conditions can be quantified and their impact on cost of transport is highlighted for future optimisation of energy consumption.

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Understanding the power requirements of autonomous underwater systems, Part I: An analytical model for optimum swimming speeds and cost of transport

Cited by 23 publications

References 34 publications

Experimental analysis of low‐altitude terrain following for hover‐capable flight‐style autonomous underwater vehicles

Experimental analysis of low‐altitude terrain following for hover‐capable flight‐style autonomous underwater vehicles

Future Vision for Autonomous Ocean Observations

Depth control for an over-actuated, hover-capable autonomous underwater vehicle with experimental verification

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