autonomous underwater robots August ste BOURGOIS OIS (PhD), Pr. - - PowerPoint PPT Presentation

August ste e BOURGO GOIS IS Resea search engineer ineer & Ph PhD studen dent august ste@f e@for

• rss

ssea-robotic tics. s.fr fr

MATS 2018, Southampton, UK November 13–14–15, 2018

Dynamic docking between autonomous underwater robots

August ste BOURGOIS OIS (PhD), Pr. Luc JAULIN

2

Introduction

Auguste BOURGOIS - Forssea Robotics – MATS 2018

AUV operations Remora project

a) ECA AUV inspecting a pipeline b) MBARI AUV mapping seafloor c) Thales AUV looking for underwater mines

AUVs can be equipped with different sensors to achieve various missions:

• Cameras
• Acoustic sensors (Multibeam, subbottom

profiler, sidescan …)

• Magnetometers

(a) (c) (b) AUV deployment from a surface vessel, using a LARS (Subsea World News)

AUVs have limitations:

• Cost/duration of deployment/recovering
• f the AUV
• Limited battery life
• Limited storage capacity

3

Introduction

AUV operations Remora project Autonomous dynamic docking of an ROV and an AUV Several issues must be solved:

• The tether influence on the ROV's trajectory is

unknown and quite unpredictable

• The AUV and the surface vessel are moving
• The ROV ought not crash onto fragile parts of the AUV
• The ROV ought not tie knots with its tether

Auguste BOURGOIS - Forssea Robotics – MATS 2018

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Vector field based approach

General idea Problem formalisation Attractive field generated by the targeted AUV Method inspired from [Le Gallic et al., 2018], adapted for time-dependent vector fields.

Auguste BOURGOIS - Forssea Robotics – MATS 2018

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Vector field based approach

General idea Problem formalisation Hypoth

• thesis:

esis:

• State of robot and target are known
• The robot has access to the target’s IMU data
• A state model is available for the robot

Vector

• r field tra

ransf sforma mation: tion: The robot follows the moving vector field using a state- feedback linearisation method ([Jaulin, 2015]): The output vector y is defined as: The command vector ur can be computed as follows: Where is the chosen error dynamics equation.

Auguste BOURGOIS - Forssea Robotics – MATS 2018

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Results

2D kinematic examples 3D dynamic example Experiments

Auguste BOURGOIS - Forssea Robotics – MATS 2018

7

Results

2D kinematic examples 3D dynamic example Experiments

Auguste BOURGOIS - Forssea Robotics – MATS 2018

8

Results

2D kinematic examples 3D dynamic example Experiments

Auguste BOURGOIS - Forssea Robotics – MATS 2018

9

Results

2D kinematic examples 3D dynamic example Experiments

Auguste BOURGOIS - Forssea Robotics – MATS 2018

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Conclusion

Auguste BOURGOIS - Forssea Robotics – MATS 2018

Promising sing met ethod hod for docki king ng problems ems

• Improved robustness w.r.t. environment’s

disturbances

• No overshoot phenomenon
• Anticipates the target’s moves
• Mathematically simple to derive/implement
• Vector field can be tuned to suit every mission

Limita tatio tions ns

• Strong hypothesis concerning the available data

and state models

• Finding a suitable vector field can be tricky

Future ure resear arch ch

• Find an elegant expression for a docking vector

field

• Develop a method based on Interval Analysis to

validate the vector field w.r.t hardware limitations References

Jaulin L. (2015) Mobile robotics ISTE WILEY Le Gallic M., Tillet J., Jaulin L. and Le Bars F. (2018) Tight slalom control for sailboat robots Presented during IRSC 2018, Southampton