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Ioannis Rekleitis
Space Robotics
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Why Space Robotics
• “Final Frontier” – “To boldly go where no robot has gone before”
• Dangerous, Tedious Work
• High scientific return on investment
• Canadian presence strong
– Canadarm
– Canadarm2
– Shuttle inspection laser and boom
– Participation in many other missions
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On-Orbit Servicing of Satellites
Work done at the Canadian Space Agency
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Ioannis Rekleitis
Guy Rouleau, Ioannis Rekleitis, Régent L'Archevêque, Eric Martin, Kourosh Parsa, and Erick Dupuis
Space Technologies Canadian Space Agency
Montréal, Canada
Presented at the 2006 International Conference on Robotics and Automation
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Motivation
• More than 19K objects bigger than 10cm in orbit (2009)
• Between 1-10cm 500K (2009)
• More than 280 satellites currently in GEO orbit
• The life span of a satellite is around 10 years
• The cost of sending even a small satellite is $10M
SOLUTION
• Use a servicing satellite to extend the life of a satellite or to de-orbit an object
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On-Orbit Servicing Opportunities
• OOS missions with Canadian involvement
Shuttle Return to Flight
XSS-11 Orbital Express
Hubble servicing study TECSAS MBS, Canadarm2, Dextre CS-417 Introduction to Robotics and Intelligent Systems
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OOS Related Missions (examples)
Russian Progress
Vehicle
Japan ETS-7
Mission
NASA DART
Mission
ESA ATV
Mission CX-OLEV Mission
DARPA NRL
SUMO Mission
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Autonomous Control
Toolbox for Reactive Autonomy
Hierarchical Finite State Machines
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High-Level Scenario
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Laboratory Setup
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Laser Camera System (LCS), Cape S/W from Neptec
The SARAH hand from Laval University
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Autonomous Capture
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Trajectory Generation of the Target Satellite
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Tracking
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A standard implementation of an extended Kalman Filter is used to track the pose of the target satellite
Signal at 2Hz Delay of 1 step EKF prediction of 1 step
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Tracking
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Capture
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Main Accomplishments
• Autonomous capture of a tumbling satellite
• Transatlantic monitoring and operation of the capture procedure
• Emulate the motion of a tumbling satellite using a 7-DOF manipulator
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Conclusions
• Cortex greatly facilitated the creation of autonomy scenarios
• The LCS from Neptec provided robust pose estimation (varying illumination conditions, obstructions)
• First step of autonomous capture in a laboratory setting
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Planetary Exploration:
• Autonomous Over-the-Horizon Navigation
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Outline
• Mars Exploration
• Background
• Main Blocks are: Terrain Modeling, Path Planning, Motion
• Control Tests from 2006 and 2007
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Exploring Mars
Ioannis Rekleitis 22
Sojourner (1997)
Spirit and Opportunity
(2004) Opportunity still
running
Phoenix (2008)
Mars Science Laboratory Curiosity (2012)
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View from Sojourner
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Missions - Pathfinder 1997
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Missions – Spirit: Day 155
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More Current Data
• Opportunity, Sol 3001 (July 03, 2012), 34.49 km
• Spirit, Sol 2210 (March 22, 2010), 7.7 km
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A Panorama from Spirit
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Phoenix in action
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Mars Science Laboratory
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For more information visit:
• http://mars.jpl.nasa.gov/msl/
• http://mars.jpl.nasa.gov/MPF/
• http://marsrovers.jpl.nasa.gov/home/
• http://phoenix.lpl.arizona.edu/index.php
• http://www.google.com/mars/
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Long-Term Goal: Autonomous Robotic Exploration
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Current Research Objectives
• Over-the-horizon Navigation in a Single Command Cycle
• Assumptions:
– Rough A Priori Knowledge:
• Localization
• Terrain
– Terrain Sensing Using LIDAR
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Local Path Planning Robot Arrives At First Way-Point Robot is Localized Operator Selects Destination Global Path Planning Local Scan Second Local Scan
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Over-the-Horizon Navigation
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Experimental Testbed 2006
• CSA Mars Terrain
– 60m x 30m
• Pioneer P2-AT Robot
• ILRIS-3D LIDAR
– 3D point cloud
– 1.5km-range (trimmed down
to ~30m)
– 40 degree FOV
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Mars Emulation Terrain 30m
60m
A B C
C B A
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Terrain Modeling • Raw Data: 3D Point Cloud
– Variable resolution
– Long shadows
• Terrain Model based on
Irregular Triangular Mesh (ITM)
– Variable Resolution (Dense where required)
– Memory-Efficient
– Preserves Topography and Useful for Navigation
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Terrain Modeling: Irregular Triangular Mesh (ITM)
Delaunay triangulation,
Cartesian coordinates
Delaunay triangulation,
polar coordinates Decimated mesh
Raw data
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Terrain Modeling: Irregular Triangular Mesh (ITM)
Raw data
I. Rekleitis McGill University
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Voronoi Diagrams For each point in the input set group all the points that are closer to it than any other input point.
I. Rekleitis McGill University
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Delaunay Triangulation Adjacent cells are connected with an edge.
I. Rekleitis McGill University
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Simulated Scan Raw data:
Obstacle shadow
I. Rekleitis McGill University
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Simulated Scan Voronoi Diagram
I. Rekleitis McGill University
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Simulated Scan Delaunay Triangulation (Cartesian Coordinates)
I. Rekleitis McGill University
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Simulated Scan Delaunay Triangulation (Spherical Coordinates)
I. Rekleitis McGill University
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Simulated Scan Delaunay Triangulation (Spherical Coordinates) -Remove triangles from shadows (use 3D information)
I. Rekleitis McGill University
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Simulated Scan Delaunay Triangulation (Spherical Coordinates) -Remove triangles from shadows (use 3D information)
Too far, classified as shadow
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2006, Scans Collected: 96
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2006, Over-the-Horizon Navigation
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2006, Over-the-Horizon Traverses Semi-Autonomous
• Successful Traverses
• A Sequence of Local Traverses
• Operator Intervention Necessary at Every Step (Semi-Autonomous)
• Achieved Traverse on the order of 150m
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2006, Over-The-Horizon Traverses
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Lessons Learned from 2006 Testing Period
• Extensive Field Testing EXTREMELY useful!
• Validate Navigation Software
• Active Vision Great under Poor Lighting
• Identify Issues Requiring further Development
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Lessons Learned
• Top level issues:
– Environment Sensor Unwieldy
• FOV Too Narrow
• Logistics a Nightmare
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Lessons Learned
• Top level issues: – Environment Sensor
Unwieldy • FOV Too Narrow
• Logistics a Nightmare
– Horizon Sometimes Much Closer than Expected
– Environment Scans Need to be Interpreted (Shadows)
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2007 Test Campaign
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Updates in the Testbed 2007
A 360o LIDAR scanner •A SICK LRF •Mounted on a pan-unit
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Scan Processing
Raw data
Delaunay triangulation,
Cartesian coordinates
Delaunay triangulation,
polar coordinates
Decimated mesh
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2007, Scans Collected: 93
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Comparison between the two LIDARs
SICK on Pan Unit
• 360o coverage
• Portable
• Easy Interface
• Limited Range
• Lower resolution
• Lower accuracy
• Low cost ~12K
ILRIS 3D • Highly accurate • Long range • High resolution • Limited field of View • Restrictive Interface • Unwieldy • Not Portable • High cost ~250K
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Irregular Triangular Mesh Decimation
Target Decimation Ratio
80% 90% 95%
2
0
0
6
Points
(mean)
31200 6530 79.00%
3440 88.86%
2090 93.09%
Triangles
(mean)
61700 12300 80.00% 6190 89.91% 3590 94.01%
2
0
0
7
Points
(mean)
111000 23400 78.91% 12500 88.72% 6700 93.69%
Triangles
(mean)
216000 43300 80.00%
21600 90.00%
10900 94.98%
Acceptable error 1.5cm
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2007, Over-the-Horizon Navigation
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Global Path Plan and Segmentation
• Produce a rough global path using the low-resolution model
• Find the portion of the global path that is inside the local scan
• Select the largest acceptable triangle closest to the furthest accessible point
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Path Planning
• Convert ITM into Connected Graph
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Path Planning
• Convert ITM into Connected Graph
• Path Planning using Graph Search Algorithms:
– Dijkstra, A*
Start
Finish
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Planning
• Convert ITM into Connected Graph
• Path Planning using Graph Search Algorithms:
– Dijkstra, A* search algorithms
• Different Cost Functions
– Number of triangles
1Q
Q
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Planning
• Convert ITM into Connected Graph
• Path Planning using Graph Search Algorithms:
– Dijkstra, A*
• Different Cost Functions
– Number of triangles
– Euclidian distance
ji xxQ
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Planning • Convert ITM into Connected Graph
• Path Planning using Graph Search Algorithms:
– Dijkstra, A*
• Different Cost Functions
– Number of triangles
– Euclidian distance
– Slope of each triangle
21
21
jj
ij
jpp
ppv
jx
jv1
jp
2
jp
Q
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Planning • Convert ITM into Connected Graph
• Path Planning using Graph Search Algorithms:
– Dijkstra, A*
• Different Cost Functions
– Number of triangles
– Euclidian distance
– Slope of each triangle
– Cross triangle slope
jx
jv
iv
ix
Q
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Path Planning
• Convert ITM into Connected Graph
• Path Planning using Graph Search Algorithms:
– Dijkstra, A*
• Cost function:
– Distance travelled
– Penalty for uphill slope
– Infinite cost for moving into too-steep triangles
– Roughness of the area under the footprint of the robot
– A* is biasing the cost towards the destination
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Path Simplification
• Path Simplification
Point-Robot
• Path Simplification
Safety Corridor
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Local Path Plan
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Motion Control
• Sensor Suite: Wheel Odometry, IMU, Heading sensor, No Visual Odometry
• 3D Pose Estimation:
Filter combines IMU+Odometry
No uncertainty estimation (currently)
• Path approximated with Catmull-Rom spline for smoothness
• Astolfi controller follows the spline trajectory
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Closed Loop Tests
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Closed Loop Tests
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Closed Loop Tests
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The Mars Terrain and Trajectories
60 m
30
m
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Fully Autonomous Navigation from flat to canyon
Goal Start
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Fully Autonomous Navigation from flat to canyon
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Fully Autonomous Navigation from flat to canyon
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Fully Autonomous Navigation from flat to canyon
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Fully Autonomous Navigation from flat to canyon
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Fully Autonomous Navigation from flat to canyon
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Fully Autonomous Navigation from flat to canyon
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Fully Autonomous Navigation from flat to canyon
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Fully Autonomous Navigation from flat to canyon
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Fully Autonomous Navigation from flat to canyon
Start Goal
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Fully Autonomous Navigation from crater to canyon
Goal
Start
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Fully Autonomous Navigation from crater to canyon
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Fully Autonomous Navigation from crater to canyon
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Fully Autonomous Navigation from crater to canyon
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Fully Autonomous Navigation from crater to canyon
Wheel odometry
Goal
Start
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Lessons Learned
• There is a need for Localization
• Limitations in the rover capabilities
• Several components require domain specific parameters
• Extensive testing extremely useful
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Future Work
• Terrain analysis – What does the robot sees?
• Open area, cluttered environment, the side of a hill?
• Different mobility platforms
• State estimation: – Implement 6DOF KF or RBPF
• Localization
• SLAM
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Conclusions
• Active vision is accurate and robust
• ITM representation is compact and accurate
• ITM useful for environmental modeling and also for path planning
• Successful Over-the-Horizon navigation an important step towards autonomy capabilities in planetary exploration
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Mars Exploration Rover (NASA)
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