safe long-range travel for planetary rovers through forward...
TRANSCRIPT
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Safe Long-Range Travel for Planetary Rovers through
Forward Sensing
12th ESA Workshop on Advanced Space Technologies for Robotics & Automation
Yashodhan Nevatia Space Applications Services
May 16th, 2013
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This research project ‘Forward Acquisition of Soil and Terrain
data for Exploration Rover (FASTER)’, running from 2011 to
2014, is supported by the European Commission through the
SPACE theme of the FP7 Programme under Grant Agreement
284419
Roland Sonsalla
Martin Frische
Thomas Voegele
Thilo Kaupisch*
Yang Gao
Said Al-Milli
Rajeev Kandiyil
Chakravarthini Saaj
Marcus Matthews
Brian Yeomans
Elie Allouis
Yashodhan Nevatia
Jeremi Gancet
Francois Bulens
Stephen Ransom
Lutz Richter
Krzysztof Skocki
*Thilo Kaupisch was affiliated with DFKI at the time part of the work reported here was performed, and is currently affiliated with DLR eV.
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Motivation
• Advances in autonomy for terrestrial robots
DARPA Grand Challenges (2004, 2005, 2007)
Google Autonomous Car
Etc…
• Planetary rovers operations
Minimal risk
Largely planned by operators so far
► Analysis, simulation based on telemetry
► Short turn around time
► Limited to line of sight operations
Still dangerous
► MER Spirit
• Future missions require greater rover capabilities!
ExoMars – mission length of ~ 9 months
Mars Sample Fetch Rover - ~ 15 km traversal
Photos: NASA/JPL-Caltech
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Objectives
• Autonomous operations allowing safe long range traversal
Forward sensing of terrain trafficability
Allow detection of hazards with minimal risk to mission rover
Components as mission ‘add on’
• System components
Scout rover
Soil sensors
► Primary rover sensors
► Scout rover sensors
► Remote sensing
Cooperative autonomy
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Operation Concept
• Primarily to pure traversal operations
Minimal or no science during traverse
• Ground Planning Phase
Identification and specification of traverse command
• On Board Command Procedures
Global Path Planning
Waypoint Traversal
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Ground Planning Phase
• Preparation for a single traverse command
• Identification of target location
Can be more than one sol away
> 200 m
• Identification of potential paths
Using orbiter data
Path as set of waypoints
► Straight line between waypoints
► Estimated (directional) cost of traversal
Multiple, interconnected paths
• Paths encoded as a directional graph
Waypoints as nodes, costs as edge weights
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Global Path Planning
Start
Traverse
O
E D
B
C
A
G
Current Location
(Start)
Target
Location
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Global Path Planning
Start
Traverse
Find best path
O
E D
B
C
A
G
• Simple graph search
• Best path: (O, A, C, E, G)
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Global Path Planning
Start
Traverse
Find best path Waypoint
Traversal
Path
found
Y
O
E D
B
C
A
G
• Sub procedure
• Traversal to next waypoint
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Global Path Planning
Start
Traverse
Find best path
Waypoint
Traversal
Path
found
Success
Target
reached
N
Y
Y
O
E D
B
C
A
G
Update edge cost
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Global Path Planning
Start
Traverse
Find best path
Waypoint
Traversal
Path
found
Success
Target
reached
N
Y
Y
O
E D
B
C
A
G
Update edge cost
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Global Path Planning
Start
Traverse
Find best path
Waypoint
Traversal
Path
found
Success
Remove edge
Target
reached
N
N Y
O
E D
B
C
A
G
Y
Update edge cost
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Global Path Planning
Start
Traverse
Find best path
Waypoint
Traversal
Path
found
Success
Remove edge
Target
reached
N
N Y
O
E D
B
C
A
G
Y
Update edge cost
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Global Path Planning
Start
Traverse
Find best path
Waypoint
Traversal
Path
found
Success
Remove edge
Target
reached
N
N Y
O
E D
B
C
A
G
Y
Update edge cost
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Global Path Planning
Start
Traverse
Find best path
Waypoint
Traversal
Path
found
Success
Remove edge
Target
reached
N
N Y
O
E D
B
C
A
G
Y
Update edge cost
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Global Path Planning
Start
Traverse
Find best path
Waypoint
Traversal
Path
found
Success
Remove edge
Target
reached
N
N Y
O
E D
B
C
A
G
Y
Update edge cost
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Global Path Planning
Start
Traverse
Find best path
Waypoint
Traversal
Path
found
Success
Remove edge
Target
reached
N
N Y
O
E D
B
C
A
G
Y
Update edge cost
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Global Path Planning
Start
Traverse
Find best path
Waypoint
Traversal
Path
found
Success
Remove edge
Target
reached
N
N Y
O
E D
B
C
A
G
Traverse
success
Y
Y
Update edge cost
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Global Path Planning
Start
Traverse
Find best path
Waypoint
Traversal
Path
found
Success
Remove edge
Target
reached
N
N Y
Y
O
E D
B
C
A
G
Traverse
fail
N
Traverse
success
Y
Update edge cost
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Waypoint Traversal
• Similar to behaviours proposed by
Volpe et. al. [1]
‘Motion-to-Goal’ and ‘Boundary-Following’
• Rovers turn towards the next waypoint
• Build elevation map
Waypoint
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Waypoint Traversal
• Similar to behaviours proposed by
Volpe et. al. [1]
‘Motion-to-Goal’ and ‘Boundary-Following’
• Rovers turn towards the next waypoint
• Build elevation map
• Remote soil sensing input for rocks
Waypoint
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Waypoint Traversal
• Similar to behaviours proposed by
Volpe et. al. [1]
‘Motion-to-Goal’ and ‘Boundary-Following’
• Rovers turn towards the next waypoint
• Build elevation map
• Remote soil sensing input for rocks
• Artificial target if needed
Waypoint
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Waypoint Traversal
• Similar to behaviours proposed by
Volpe et. al. [1]
‘Motion-to-Goal’ and ‘Boundary-Following’
• Rovers turn towards the next waypoint
• Build elevation map
• Remote soil sensing input for rocks
• Artificial target if needed
• Path planning
Waypoint
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Waypoint Traversal
• Similar to behaviours proposed by
Volpe et. al. [1]
‘Motion-to-Goal’ and ‘Boundary-Following’
• Rovers turn towards the next waypoint
• Build elevation map
• Remote soil sensing input for rocks
• Artificial target if needed
• Path planning
• No path – Obstacle circumnavigation
Rovers allowed to rotate slightly
Waypoint
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Waypoint Traversal
• Scout begins ‘forward sensing’
Path updates at scout location as needed (eg. hazard detection)
Extension of elevation map on reaching end of path
Constrained to within line of sight of primary rover
• Primary rover traversal
Path update from primary rover location on detection of hazard
Scout returns to primary rover location
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Waypoint Traversal Failure
• No path found, rovers at waypoint
No new nodes needed
A
B
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Waypoint Traversal Failure
• No path found, rovers at waypoint
No new nodes needed
• Hazard detection by scout
Addition of two connected nodes
Scout to primary, or vice versa
A
B
A1
A2
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Waypoint Traversal Failure
• No path found, rovers at waypoint
No new nodes needed
• Hazard detection by scout
Addition of two connected nodes
Scout to primary, or vice versa
• Hazard detection by primary rover
Single new node
Scout to primary rover
A
B
A1
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Waypoint Traversal Failure
• No path found, rovers at waypoint
No new nodes needed
• Hazard detection by scout
Addition of two connected nodes
Scout to primary, or vice versa
• Hazard detection by primary rover
Single new node
Scout to primary rover
• New nodes are connected
Last waypoint
Other nodes in line of sight? A
B
A1
C
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Cooperative Autonomy & Software
• E3 level of autonomy
Adaptive mission operations on-board
• Partial support for E4 level of autonomy
Operation concept provides for goal based traversal
• Scout Rover
Minimal autonomy
► Path following
► Return to primary
► Proprioception
• Primary rover
Subsystem architecture based on GenoM with ROS modules
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Primary Rover Software Architecture
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Representative Subsystems
• Task Execution Controllers
Simple on board procedure engines
Predefined procedures
• Health Management
Offline analysis based fault detection and recovery
• Data Management
Simplified data repository supporting pull/push
• Communication
Wireless communication with scout
Simulated communication with ground
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Representative Subsystems
• Locomotion Controller
Implements drive commands
TCP interface for Bridget Controller
• Sensor Managers
Point Grey XB3 Stereo Camera
XSens MTi IMU
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SSS Software Chain
• Interface with soil sensing system
Traversability classification
Based on outputs of multiple sensors
• Rock tracking
via Remote Sensing
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Task Planner
• Planning and monitoring high level tasks
• Symbolic planner
Hierarchical Task Networks
Domain configurable, multi agent framework [2]
• Generation of interleaved plans
Multiple, concurrent action sequences
Query functional modules for cost estimates
Re-planning, including contingency actions
• Validation of plans with regard to resources
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Scout Localization
• Tracking 6DOF scout pose in images
• Combined with scout proprioception
• SURF feature based:
3D descriptor database
Generation of image-model
correspondences
• Marker based:
ARToolkit for benchmarking
Custom marker configurations
Single Marker Multi Marker
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GNC: Localization
• Wheel and/or Inertial Odometry
• Visual Odometry
• Rock Tracking
Output from Remote sensing
Sparse feature SLAM being investigated
• Map matching
Detailed local elevation map built by rover
Low resolution orbiter height map
Bounds for odometry error
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GNC: Mapping & Path Planning
• Elevation maps based on stereo camera images
Filtering
Iterative Closest Point for 3D data fusion
• D* based local path planning, trajectory fitting
Primary Rover
Stereo Images
Scout Rover
Stereo Images
Point Cloud
Point Cloud
Scout
Localization
Point Cloud
Merging (ICP)
DEM Generation Path Planning
Remote Soil
Sensing
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System Validation
Simulation
Autonomy
Field Trials
Integrated
Field Trials
• Rapid development & testing
• Gazebo
Multi-robot outdoor simulation
Open Dynamics Engine
DARPA Robotics Challenge
• HiRISE imagery from MRO
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System Validation
Simulation
Autonomy
Field Trials
Integrated
Field Trials
• Iterative validation of algorithms
• Preliminary integration with scout
• Realistic datasets
• Clearpath Husky A200
Beez Quarry, Belgium
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System Validation
Simulation
Autonomy
Field Trials
Integrated
Field Trials
• Integrated system validation
Soil sensors
Bridget locomotion breadboard
• Field trials at Stevenage
Second half of 2014
Photo: PRoVisG Field Trial, 2011
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Thank you!
Yashodhan Nevatia
Work Package Leader Space Applications Services
Chakravarthini Saaj
Technical Manager University of Surrey
Thomas Vögele
Project Coordinator German Research Center for
Artificial Intelligence
https://www.faster-fp7-space.eu/
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References
[1] Volpe, R., Estlin, T., Laubach, S., Olson, C., & Balaram, J. (2000). Enhanced
mars rover navigation techniques. In Proc. IEEE International Conference on
Robotics and Automation, 2000 (ICRA'00), IEEE, Vol. 1, pp. 926-931.
[2] R. Kandiyil and Y. Gao, “A Generic Doman Configurable Planner using HTN for
Autonomous Multi-Agent Space System,” in Proc. 11th International
Symposium on Artificial Intelligence, Robotics and Automation in Space, Turin,
Italy, 2012.
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MSR Mission Outline
Mission Surface Phases Activities Duration Units
Post Landing Checkout
Comms establishment & HK status 4 sols
Initial Post landing checkout 4 sols
Egress 4 sols
Post-landing checkout duration 12 sols
Preparation for Departure
from Landing Point
SFR commissioning 5 sols
Landing Site local exploration 2 sols
Pre-excursion duration (sols) 7 sols
Sample Cache Acquisition
Identification of target location 1 sols
travel to target location 3 sols
Verification & confirmation of target 2 sols
Sample Acquisition 1 sols
Sample Cache Acquisition Duration 7 sols
Cache transfer to MSR Lander
Identification of target location 1 sols
travel to target location 2 sols
cache Transfer 1 sols
Cache Transfer to MSR Duration 4 sols
Contingencies
Conjunction Allocation (no ops) 20 sols
Dust storm or operational contingency 20 sols
Total Contingency 40 sols
Total Mission Operations duration 70 sols
Nominal Mission Duration 180 sols
Duration left for traverse 110 sols
Traverse Distance 15 km
Traverse Margin 1.3
Minimum Rover speed 177 m/sol
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Scout Forward Sensing
1
2 3
• Three scenarios considered with different parameters
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Scout Forward Sensing
• Measurement time of 60 s
• Step pattern is too slow to achieve desired daily traverse
• Scenario 3 chosen as baseline
Most plausible in terms of mission size and complexity
turns
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Extended Operations
• Exploration or scientific tasks
Very useful, but considered out of scope
• Long Term Scouting
beyond range of Primary Rover sensors/communication
delays in case of wrong trafficability from scout sensors
• Increased requirements for Scout
Fully autonomous operation
► Self localization and task planning
Increased power requirements
• Increased mission complexity
Relative localization between scout path and primary rover
Recharging of scout batteries
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Remote Sensing
• Semantic feature detection rather than pixel-based feature
detection
• Three approaches are currently being investigated:
o Supervised machine learning classifiers (SVM & Boosted
LDA):
-1 1
Positive Patch
F(x) = 1
Negative Patch
F(x) = -1
Stage-1
(Learning)
Stage-2
Classification
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Remote Sensing
Stage-1 Stage-2
• Blob Detection, classification, and tracking
• Saliency Detection, classification, and tracking