Zürich Autonomous Systems Lab Cedric Pradalier [email protected] ICRA Workshop on Planetary Rovers, May 2010
Zürich Autonomous Systems Lab Cedric Pradalier [email protected] ICRA Workshop on Planetary Rovers, May 2010.
Mar 27, 2015
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Welcome to Anchorage
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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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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
ZürichAutonomous Systems Lab
Overview, Crab, Eurobot EGP Prototype
Exomars Breadboard
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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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Nanokhod
Shrimp & Solero◦ Passive suspension
systems◦ 6 motorized wheels◦ 2 steering
◦ Very good terrainability!
ASL rovers background
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RCL-E
RCL-C
CRAB
Exomars: Pre-study phase A
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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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ExoMars Breadboard
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ExoMars Breadboard
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Authorization denied…
Test plan and results
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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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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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Mechanical design
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Mechanical design
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Implementation
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Suspension
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Integration
880kg, without astronaut…
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Integration
ZürichAutonomous Systems Lab
Optimised Torque ControlLearning what comes ahead
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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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Control loop
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Test setup and hardware
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Results
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Results
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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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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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Outline
SOFTWARE
HARDWARE Actuators
Controller
Path Planning Prediction
Learning
Database
ProBT
Near to farDelay
Local Sensors Remote Sensors
Obst. Det.
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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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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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Outline
SOFTWARE
HARDWARE Actuators
Controller
Path Planning Prediction
Learning
Database
ProBT
Near to farDelay
Local Sensors Remote Sensors
Obst. Det.
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Prediction Process
Remote Subspace Local Subspace
Fr = 0.5
Prediction
20% 50% 30%
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Path planner – E*◦ Wavefront propagation
Navigation function◦ Gradient descent◦ Propagation cost
Process
Adaptive navigation
assumption T = 1
Image acquisition
Fl prediction Propagation costs
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Outline
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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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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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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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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 MRTI
20% MRTI 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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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
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Questions?
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