Development of highly mobile planetary rovers: from hardware optimisation to embedded software

1. Summer School – FSRM / IMT Neuchatel Development of highly mobile planetary rovers: from hardware optimisationto embedded software Cedric Pradalier Cedric.pradalier@mavt.ethz.ch ICRA Workshop on Planetary Rovers, May 2010

2. Welcome to Anchorage

3. 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

4. Autonomous Systems Lab • 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

5. Hardware Platforms Overview, Crab, Eurobot EGP PrototypeExomars Breadboard

6. ASL – ETH Zurich • 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

7. ASL rovers background • Nanokhod • Shrimp & Solero • Passive suspension systems • 6 motorized wheels • 2 steering • Very good terrainability!

8. RCL-E RCL-C CRAB Exomars: Pre-study phase A

9. 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

10. ExoMars Breadboard

11. ExoMars Breadboard

12. Test plan and results • Authorization denied…

13. EGP Rover Prototype • 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 Picture from Didot et al. IROS’07

14. EGP Rover – Requirements • 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 !!!

15. Mechanical design

16. Mechanical design

17. Implementation

18. Suspension

19. Integration 880kg, without astronaut…

20. Integration

21. Software developments Optimised Torque Control Learning what comes ahead

22. 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

23. Control loop

24. Test setup and hardware

25. Results

26. Results

27. Summer School – FSRM / IMT Neuchatel Adaptive mobile robot navigation based on online terrain learning Ambroise Krebs ambroise.krebs@mavt.ethz.ch

28. ? Approach: Basic concept • 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?

29. Approach: Architecture overview • RTILE Rover-Terrain Interactions Learned from Experiments Path Planning Obst. Det. Prediction Learning Database Controller ProBT SOFTWARE Near to far Delay HARDWARE Actuators Local Sensors Remote Sensors Trafficability & Terrainability Traversability

30. Outline Path Planning Obst. Det. Prediction Learning Database Controller ProBT SOFTWARE Near to far Delay HARDWARE Actuators Local Sensors Remote Sensors

31. Near to far • Data acquisition: 2D example • Grid based approach • Remote Image acquisition • Local Position of the wheels • Samples When learning occurs Remote Local Features association Samples can be used for the learning mechanism.

32. 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

33. Outline Path Planning Obst. Det. Prediction Learning Database Controller ProBT SOFTWARE Near to far Delay HARDWARE Actuators Local Sensors Remote Sensors

34. 20% 50% 30% Prediction • Process Fr = 0.5 Remote Subspace Local Subspace Prediction

35. Adaptive navigation • Path planner – E* • Wavefront propagation • Navigation function • Gradient descent • Propagation cost • Process assumptionT = 1 Image acquisition Fl prediction Propagation costs

36. Outline

37. Propagation costs function • 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 Start Goal ? Very bad Good Very good

38. 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

39. RTILE: Results “complete” • Test of the complete approach

40. 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

41. 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

42. Questions? Have a nice workshop…