Zürich
Autonomous Systems Lab
Cedric Pradalier
Cedric.pradalier@mavt.ethz.ch
ICRA Workshop on Planetary Rovers, May 2010
Zürich
Autonomous Systems Lab
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Welcome to Anchorage
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Autonomous Systems Lab
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Outline
Autonomous Systems Lab
Brief summary of the space
-
related activities
Hardware platforms
◦
Eurobot EGP Prototype
◦
ExoMars breadboard
Embedded Software
◦
Lowering friction requirements using optimised torque distribution
◦
Learning what’s come ahead
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Autonomous Systems Lab
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Lab of Pr. Siegwart
◦
www.asl.ethz.ch
◦
ETH Zürich
–
Switzerland
◦
20 PhD / 40 Total
Education
◦
Lectures:
Bachelor / Master
◦
Project supervision
Research
◦
Vision:
Create machines that know what they do
◦
Three research line:
The design of robotic and mechatronic systems
Navigation and mapping
Product design methodologies and innovation
Autonomous Systems Lab
Zürich
Autonomous Systems Lab
Overview, Crab,
Eurobot EGP Prototype
Exomars Breadboard
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Autonomous Systems Lab
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◦
Micro Air Vehicles
◦
Walking and Running
Quadruped Robots
◦
Service Robots
◦
Autonomous Robots/Cars
for Inner City Environments
◦
Inspection Robots
◦
Space Robots for Planetary
Exploration
◦
Autonomous sailing/electric
boats
ASL
–
ETH Zurich
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Autonomous Systems Lab
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Nanokhod
Shrimp & Solero
◦
Passive suspension
systems
◦
6 motorized wheels
◦
2 steering
◦
Very good terrainability!
ASL rovers background
Zürich
Autonomous Systems Lab
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RCL
-
E
RCL
-
C
CRAB
Exomars: Pre
-
study phase A
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Autonomous Systems Lab
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Platform
◦
Passive suspension
◦
6 Motorized wheels
◦
4 Steering
Mobile robots
◦
Confronted to environments
which are unknown
◦
Difficulty to:
Model before
-
hand the
environment of the rover.
Predict its terrain interaction
characteristics.
CRAB rover
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Autonomous Systems Lab
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ExoMars Breadboard
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Autonomous Systems Lab
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ExoMars Breadboard
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Autonomous Systems Lab
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Authorization denied…
Test plan and results
Zürich
Autonomous Systems Lab
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Eurobot:
◦
Multi
-
arm astronaut assistant
◦
Developed by Thales (and others?) for ESA
EGP = Eurobot Ground Prototype
◦
Put some wheels and perception under the Eurobot
◦
Experiment on the concept of an astronaut assistant
EGP Rover Prototype
Picture from Didot et al. IROS’07
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Autonomous Systems Lab
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Ability to carry and power Eurobot (150Kg)
Ability to transport an astronaut in full EVA (100Kg)
Power autonomy for multiple hours, fast recharge
◦
150kg of lead
-
acid batteries
Ability to perceive its surrounding, plan path, follow an astronaut, using a stereo
-
pair
Rough terrain capabilities (15 deg slopes, 15cm steps)
Cheap !!!
EGP Rover
–
Requirements
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Autonomous Systems Lab
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Mechanical design
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Autonomous Systems Lab
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Mechanical design
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Autonomous Systems Lab
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Implementation
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Autonomous Systems Lab
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Suspension
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Autonomous Systems Lab
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Integration
880kg, without astronaut…
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Autonomous Systems Lab
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Integration
Zürich
Autonomous Systems Lab
Optimised Torque Control
Learning what comes ahead
Zürich
Autonomous Systems Lab
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Optimised torque control
Principle
◦
It is possible to put more torque on
wheel with more load
Requirements
◦
Measurement of contact point on each
wheel
◦
Static model to deduce the wheel load
from the contact points and the rover
state
Results submitted to IROS’10
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Autonomous Systems Lab
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Control loop
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Autonomous Systems Lab
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Test setup and hardware
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Autonomous Systems Lab
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Results
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Autonomous Systems Lab
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Results
Zürich
Autonomous Systems Lab
Ambroise Krebs
ambroise.krebs@mavt.ethz.ch
Zürich
Autonomous Systems Lab
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Two types of sensors needed
◦
Remote sensors
→
Remote Terrain Perception data
◦
Local sensors
→
Rover
-
Terrain Interaction data
Data association
Prediction
◦
What are the Rover
-
Terrain Interaction characteristics?
Approach: Basic concept
?
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Autonomous Systems Lab
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Delay
Approach: Architecture overview
RTILE
Rover
-
Terrain Interactions Learned from Experiments
SOFTWARE
HARDWARE
Actuators
Controller
Path Planning
Prediction
Learning
Database
ProBT
Near to far
Local Sensors
Remote Sensors
Obst. Det.
Trafficability & Terrainability
Traversability
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Autonomous Systems Lab
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Outline
SOFTWARE
HARDWARE
Actuators
Controller
Path Planning
Prediction
Learning
Database
ProBT
Near to far
Delay
Local Sensors
Remote Sensors
Obst. Det.
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Autonomous Systems Lab
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Data acquisition: 2D example
Grid based approach
◦
Remote
Image acquisition
◦
Local
Position of the wheels
◦
Samples
When learning occurs
Near to far
Samples
can be used for the learning mechanism.
Remote
Local
Features association
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Autonomous Systems Lab
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Bayesian model
Goal
◦
Local features predicted based on remote features
Bayesian model
◦
Joint distribution and decomposition
◦
Introduce abstraction classes and
Question
→
Class association
Local classification
Remote classification
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Autonomous Systems Lab
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Outline
SOFTWARE
HARDWARE
Actuators
Controller
Path Planning
Prediction
Learning
Database
ProBT
Near to far
Delay
Local Sensors
Remote Sensors
Obst. Det.
Zürich
Autonomous Systems Lab
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Prediction
Process
Remote Subspace
Local Subspace
F
r
= 0.5
Prediction
20%
50%
30%
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Autonomous Systems Lab
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Path planner
–
E*
◦
Wavefront propagation
Navigation function
◦
Gradient descent
◦
Propagation cost
Process
Adaptive navigation
assumption
T
= 1
Image acquisition
F
l
prediction
Propagation costs
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Autonomous Systems Lab
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Outline
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Autonomous Systems Lab
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Rover
-
Terrain Interaction metric
◦
◦
The smaller, the better
Remote feature space
◦
Camera
◦
Color description
Trajectory adaptation
Absolute cost method
◦
Idea of tradeoff between
What can be gained in terms of , meaning
The deviation it imposes from the default trajectory
◦
Dynamically adapts to the terrain representation
Propagation costs function
Very bad
Very good
Good
Start
Goal
?
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Autonomous Systems Lab
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RTILE: Results
Adaptive navigation
Test environment in Fluntern
◦
3 terrains
Grass
softest
(best)
Tartan
Asphalt
hardest (worst)
•
Automatically driven
•
6 cm/s
•
No prior
•
Learning every 6 m
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Autonomous Systems Lab
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RTILE: Results
“
complete
´
Test of the complete approach
Waypoints
x [m]
y [m]
0
0.0
0.0
1
15.0
0.0
2
15.0
-
15.0
3
0.0
-
15.0
4
0.0
-
2.5
5
12.5
-
2.5
6
12.5
-
15.0
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Autonomous Systems Lab
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Summary
RTILE: Rover
-
Terrain Interactions Learned from Experiments
◦
End
-
to
-
end approach
Online learning
Navigation adapted accordingly
Integrated within the CRAB platform
◦
Tradeoff distance vs M
RTI
20% M
RTI
improvement
10% longer distance
◦
Terrain description
Consistent interaction with E*
Dynamical adaptation of the propagation costs
RTILE improves the rover behavior
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Autonomous Systems Lab
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Future work
Improvements
◦
Add feature spaces (subspaces) for a better terrain description
◦
Use additional sensors
Local:
Tactile wheels, Microphones, and so on
…
Remote:
Google earth map (
increase FOV
), Lidar
◦
Improved features
Remote:
Fourier based, Co
-
occurrence matrix, and so on
…
◦
Learning
Clustering step (GWR)
Outlook
◦
Energetic description
◦
Learn as well the behavior of the rover
Zürich
Autonomous Systems Lab
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