Unmanned surface vehicle energy consumption modelling under various realistic disturbances integrated into simulation environment

doi:10.1016/j.oceaneng.2020.108560

Ocean Engineering

Volume 222, 15 February 2021, 108560

https://doi.org/10.1016/j.oceaneng.2020.108560 Get rights and content

Highlights

•
The unmanned maritime drones' autonomy is limited by their battery capacities.
•
Several available approaches are used to reduce their consumption.
•
The existing solutions are not feasible in complicated environments or huge scenarios.
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The problem is solved by developing a more realistic power model of the marine drones.
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A robust simulator is used to manage and estimate the consumption of marine drones.

Abstract

Energy consumption estimation and management of the maritime Unmanned Surface Vehicles (USV) is an important issue to deal with energy minimization techniques such as path planning, tasks scheduling, etc. In this paper, we introduce the energy consumption parameter in USV simulation through three contributions: 1) An analytic USV's energy consumption model is developed based on the three-degrees-of-freedom dynamic model of surface vessels. 2) A reverse engineering approach is proposed to identify the previously used dynamic model parameters based on a set of scenarios executed within a recent simulation environment. 3) The simulator engine is enriched with the consumption modelling tools such that the power absorbed by the USV is instantaneously calculated and returned; thus, the required energy of any predefined scenario is available as a new simulation result.

Introduction

Autonomous Underwater Vehicles (AUV) and Unmanned Surface Vehicles (USVs) and are a promising solution for different marine applications such as: port navigation, rescue, environment control, military missions, ocean maps generation, marine scientific research, etc. Their main advantage is the ability to evolve in environments where humans are not able to intervene safely, in addition to their cost and continuous activity. Actually, AUVs and USVs and operate generally in difficult environment conditions needing precision, reliability, and autonomy. To provide solutions to these critical requirements, scientific community is more and more focusing their research in marine vehicles field and their applications (Jorge et al., 2019; Zhang et al., 2020).

One of the most challenging issues faced when dealing with AUVs or USVs is the autonomy problem. Like any autonomous robot, the USVs capabilities to execute their missions are limited by the capacity of their batteries. However, this issue is more critical in case of USV since they can operate in environment where frequent battery discharges may impact considerably the good achievement of the executed mission especially that their autonomy depends on two complex and variable environment parameters: wind and water current. Thus, this energy constraint makes the consumption optimization one of the most important challenges for mobile robots' research community. The energy limitation is therefore becoming a significant constraint to either solve path planning problems, to schedule tasks, or to design an energy-based optimal controller. The energy consumption estimation of the USVs can be achieved by measuring instantaneously the real power absorbed by the vehicle using on-board sensors (mainly: current or power sensors). However, this may require additional hardware which affects the drones’ behaviour due to the additional weights, as well as this approach is usually infeasible and would be very costly in case of too many scenarios or huge ones and not possible of course during the USV design phase. Another approach consists to use of simulation environment to test and validate USVs autonomy and their consumption at early phase of mission planning which allows saving cost and time. The main drawback of the existing solutions is the use of simple and non-realistic energy models which are not representative of real-life scenarios. Hence, our proposed solution begins by developing a more realistic power model of the USVs based on their dynamics and to integrate it into one of the robust USV simulators that exist. There are many simulation environments for USVs such as the one presented in (Paravisi et al., 2019) which is an open source, recent, and very robust simulator that simulates different USV types under realistic disturbances. However, none of the available simulators considers the energy consumption parameter in their engines.

In this paper, we introduce the energy consumption parameter in USVs simulation. Our methodology is general but implemented on the open-source simulation environment presented in (Paravisi et al., 2019). This last one is a recent Robot Operating System (ROS) and Gazebo-based software package that simulates virtual USVs in realistic environment and disturbances. Actually, we introduce the energy consumption parameter into the simulator through the following steps: First, an analytical USVs energy consumption model is developed based on the three-degrees-of-freedom (3-DOF) dynamic model given in (Fossen, 2011) by ignoring the wave effect. Then, we explain how to identify the constant coefficients of the used dynamic model based on a set of scenarios executed within the simulation environment using a reverse engineering approach (Canfora et al., 2011). We apply this methodology particularly for Lutra-Prop boat, a representative differential drive USV available in the used simulation environment. Thereafter, the energy model obtained from the two previous steps is integrated into the simulator engine. Finally, we run different simulation scenarios and analyse the obtained estimated energy consumption results in a short time and without any hardware requirement. In addition, the presented approach allows us to evaluate the effect of the USV speed on its consumption as given later in this paper.

The rest of the paper sections is organized as follows: section 2 contains the related work. The USV dynamics with and without disturbances are involved in section 3. Power consumption modelling is given in section 4. Section 5 illustrates the model parameters identification for the Lutra-prop differential drive USV using the reverse-engineering approach. Section 6 demonstrates the integration of the obtained power model into its corresponding virtual package within the USV simulator. Section 7 includes the simulation results of seven different realistic scenarios, it involves as well as a representation of the effect of the USV speed on its power consumption. Finally, the paper is ended with a general conclusion and references.

Section snippets

Related works

Many recent works have been published aiming to model, to estimate, and to minimize the energy consumption of different mobile robots (Wahab et al., 2015; Hou et al., 2019a, 2019b; Canfield et al., 2019; Jaramillo-Morales et al., 2019, 2020; Zhang et al., 2019). Accordingly, most of the researchers are considering the energy efficiency to be the key on autonomous robots' performances since they are constrained by their batteries' capacity limitations. From this literature, we notice that most

The 3-DOF USV dynamics

In real scenarios, a marine drone can move longitudinally, laterally, and vertically, it can also move circularly around each axis producing roll, pitch, and yaw movements. This type of motion system is known as: six-degrees-of-freedom (6-DOF) system. In order to simplify our analysis, we neglect the marine wave effect; thus, the vertical motion, roll rotation, and pitch rotation are ignored because the energy consumed in these dimensions is much lower. Therefore, the system is reduced to a

USV power consumption modelling

As previously mentioned, the consumed power of a USV is divided into two parts: the static power due to static consumption (on-board computer and electrical losses) and the dynamic power involving the thrust power. Thus, the total consumption of a given scenario is obtained by integrating the instantaneous power in the scenario's duration interval. In other word, the total energy consumption of the USV can be modelled by Eq. (20). $E_{U S V} = \int P_{U S V} . d t$

As previously discussed, $P_{U S V}$ is the total power

Model parameters identification approach applied to Lutra-prop boat

This section details the approach used to identify the added mass parameters $m_{11}$ , $m_{22}$ , $m_{33}$ and the dynamic coefficient $d_{11}$ , $d_{22}$ , $d_{33}$ of the case of study drone to obtain its full 3-DOF power model given in Eq. (24). The added mass parameters are identified based on their estimated expressions given in (Muske et al., 2008) and (Li et al., 2019). Whereas a reserve engineering approach (Canfora et al., 2011) is used to identify the dynamic coefficients from a recent USV simulator (Paravisi et al.,

Energy consumption model integration into USV simulation environment

After establishing the analytical consumption model of a differential drive surface drone and identifying its parameters using several approaches, the obtained model can be integrated into the USV simulator to verify and to approve its correctness and usefulness. This section illustrates the approach used to enrich the USV simulator with power and energy management tools to instantaneously calculate and monitor the USV's dynamic power consumption. The used simulator is an open-source

Simulation and results

In order to show the effectiveness and the usefulness of the energy modelling, identification, and integration into the simulation environment approaches, different scenarios are conducted with and without wind and water current disturbances. In this section, we go through seven different and independent scenarios, the surge velocity $u (t)$ and the calculated dynamic power absorbed by the USV are instantaneously recorded for each scenario by neglecting the static power. These scenarios have been

Conclusion

The energy consumption model of the Unmanned Surface Vehicles was established first and presented as a function of their environments (speed, wind, water current, etc.) based on the 3-DOF dynamic model of surface vessels. A reverse-engineering approach was applied on a recent and very robust USV simulation environment to identify the dynamic model parameters of the Lutra-prop USV; thus, its complete energy consumption model is deduced. The wind dynamic parameters were obtained from the Gazebo's

CRediT authorship contribution statement

Walid Touzout: Supervision, Methodology, Writing - original draft, Writing - review & editing. Yahia Benmoussa: Writing - review & editing. Djamel Benazzouz: Supervision, Project administration. Erwan Moreac: Software, Resources. Jean-Philippe Diguet: Methodology, Writing - review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The authors would like to thank the General Directorate for Scientific Research and Technological Development (DGRSDT) of Algeria for its support. Special thanks go to Guy Gognat and Christine Chauvin of the University of South Brittany of France for their valuable encouragement.

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Unmanned surface vehicle energy consumption modelling under various realistic disturbances integrated into simulation environment

Highlights

Abstract

Introduction

Section snippets

Related works

The 3-DOF USV dynamics

USV power consumption modelling

Model parameters identification approach applied to Lutra-prop boat

Energy consumption model integration into USV simulation environment

Simulation and results

Conclusion

CRediT authorship contribution statement

Declaration of competing interest

Acknowledgments

Energies

IFAC-PapersOnLine

Ocean Eng.

J. Syst. Architect.

Math. Comput. Simulat.

Prediction and experimental validation of power consumption of skid-steer mobile robots in manufacturing environments

J. Intell. Rob. Syst.

Achievements and challenges in software reverse engineering

Commun. ACM

Energy optimal depth control for long range underwater vehicles with applications to a hybrid underwater glider

Aut. Robots

ROS Robotics by Example: Learning to Control Wheeled, Limbed, and Flying Robots Using ROS Kinetic Kame

Handbook of Marine Craft Hydrodynamics and Motion Control

Energy modeling and power measurement for three-wheeled omnidirectional mobile robots for path planning

Electronics

Predictive power estimation for a differential drive mobile robot based on motor and robot dynamic models

Energy estimation for differential drive mobile robots on straight and rotational trajectories

Int. J. Adv. Rob. Syst.

Design and verification of heading and velocity coupled nonlinear controller for unmanned surface vehicle

Sensors

A survey on unmanned surface vehicles for disaster robotics: main challenges and directions

Sensors

Design and use paradigms for gazebo, an open-source multi-robot simulator