1. Autonomous Systems Lab 1 Evaluation and Optimization of Rover Locomotion Performance Machines that know what they do Thomas Thueer & Roland Siegwart ICRA’07, Rome Workshop on Space Robotics

2. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 2 Outline  Locomotion Concepts  Metrics  Aspects Locomotion Performance  Example: Rover Comparison - Simulation & Hardware  Improving Locomotion Performance  Conclusion and Outlook

3. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 3 Locomotion Concepts  How to design wheeled rovers for rough terrain?

4. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 4 Characteristics of Locomotion Mechanisms  Trafficability: capacities to drive over a loose terrain  Main parameters: Wheel-Ground Contact Distribution of Mass  Maneuverability: mainly the steering capacities  Locomotion mechanism (steering of wheels)  Type of contact with ground  Terrainability: capacities to cross obstacles and maintain stability  Locomotion mechanism  Mass distribution  Type of contact, number and distribution of contact point

5. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 5 Classification of Locomotion [M. Yim, 1995]  Basic motion concept:  Roll-Legged: Rolling type motion Wheel, tracks  Swing-Legged: Walking type motion Legs  Temporal characteristic of contact  Continuous-Footed: Continuous ground contact Rolling, snake-like motion  Discrete-Footed: Discrete ground contact Walking like contact, jumping  Type of contact  Little-Footed: Point contact with ground Idealized point contact of wheel or leg  Big-Footed: Surface contact with ground Track-type contact, real wheel (tire), big foot of a walker

6. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 6 Most popular locomotion concepts  SDB (Swing-legged, Discrete- and Big-footed)  Most today's humanoid robots  Adapted for flat ground  Stability very critical in rough terrain  SDL (Swing-legged, Discrete- and Little-footed)  Walking robots with 4 or 6 legs  Reasonably good stability with 6 and more legs  System and control very complex  RCL (Roll-legged, Continuous- and Little-footed)  Wheeled rover with rigid tiers  Good stability if # of wheels and suspension is adapted  Good maneuverability  RCB (Roll-legged, Continuous- and Big-footed)  Tracked rovers  Good stability and tracking  Bad maneuverability

7. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 7 Comparison of locomotion concepts Compe- tence Concept TrafficabilityManeuver- ability TerrainabilitySystem Complexity Control Complexity SBD e.g humanoid with big foots okgoodbadhighvery high SDL e.g. 6 leg robot very goodgood very highhigh RCL EPFL or RCL E wheel rover okgoodok-goodlow RCB Nanokhod goodbadoklow

8. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 8 Wheeled Rovers (RCL): Concepts for Object Climbing Purely friction based Change of center of gravity (CoG) Adapted suspension mechanism with passive or active joints

9. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 9 Catalog of Existing Solutions I

10. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 10 Catalog of Existing Solutions II

11. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 11 Metrics  Necessary for proper comparison of different systems  “Know what conclusion you want to derive”  Requirements  Precise definition  Measurable  Objectivity / independent from specific parameters  Ideally available in simulation and reality  Apply to normalized systems  Absolute / relative comparison  Level of accuracy (requirements, level of knowledge of final design)

12. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 12 Metrics – Overview  Metrics for different aspects of performance  Terramechanics  Obstacle negotiation capabilities  Metrics for sub-systems  Evaluation independently from rover  Same performance of sub-system on different rovers  E.g. Rover Chassis Evaluation Tools (RCET) activity for wheel characterization

13. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 13 Metrics Terramechanical & Geometrical Aspects  Analysis of wheel ground interaction based on Bekker  Drawbar pull Equal for all rovers if normalized, independent from suspension  Slope gradeability Depends on suspension that defines normal force distribution on slope  Static stability  See slope gradeability  Geometrical analysis not sufficient!

14. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 14 Metrics Obstacle Negotiation (Terrainability)  Minimum friction requirement  Minimizing risk of slippage/getting stuck in unknown terrain  Optimization: equal friction coefficients  Minimum torque requirement  Minimizing weight and power consumption  Slip  Bad for odometry, loss of energy

15. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 15 Example: Rover Comparison - Simulation & Hardware  Comparison of different rovers  CRAB (sim. & HW)  RCL-E (sim. & HW)  MER – rocker bogie type rover (sim.)

16. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 16 Example: Rover Comparison – Simulation Setup  Performance Optimization Tool (2DS – RCET)  Static, 2D analysis  Fast calculation allows for parametrical studies: optimization of structures  Over actuated systems: optimization of wheel torques  Results reflect full potential of structure (not influenced by parameter tuning, control algorithm)  Simulations  Benchmark: step obstacle (tough task for wheeled rovers)  Rovers normalized (mass, wheels, track, CoG, load dist.)  Models with respect to breadboard dimensions/weight

17. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 17 Example: Rover Comparison – Simulation Results CRABRCL-E MER FWD MER BWD Required friction coefficient [-] 0.640.950.571.0 Max. T [Nm] 6.07.36.78.9 Required friction coefficient [-] Required torque [Nm]  Equally good performance of CRAB and MER  Different forward and backward performance of asymmetric systems as potential drawback

18. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 18 Rover Comparison – Experimental Setup  Rovers  Modular design: same wheels and electronics  GenoM software framework  Motors: Maxon RE-max 22 Watt; EPOS controllers  Equal footprint (0.65 m), similar weight (32-35 kg)  Test runs  Control: velocity, velocity with wheel synchronization  Two types of obstacle coating (rough, carpet-like)  Step (wheel diameter high)  At least 3 runs; log of currents, encoder values

19. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 19 Example: Rover Comparison – Experimental Results (1)  CRAB  Success rate:SR = 100 %  Slippage: Slip = 0.3 m  RCL-E  Success rate:SR = 0 % Wheels blocked because of insufficient torque  Modification of controller settings: Maximum current increased (2.5 A  3.5 A; 8.6 Nm  12 Nm)  Success rate:SR = 47 %  Slippage:Slip = 0.41 m

20. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 20 Example: Rover Comparison – Video of Testing Hardware tests with CRAB and RCL-E

21. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 21 Example: Rover Comparison – Experimental Results (2)  Rover: CRAB  Successful test run  Peaks indicate obstacle climbing of wheels  Current graph  Saturation at 2.5A  Negative currents occur  Distance graph (encoders)  Normal inclination  wheel moving or slipping  Reduced inclination  wheel blocked saturation wheels blocked negative currents

22. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 22 Example: Rover Comparison – Experimental Results (3)  Rover: RCL-E  Failed test: rover blocked (current limit at 2.5 A)  Rear wheel saturated  Front and middle wheel slip  Successful test (current limit at 3.5 A)  Current back wheel > 2.5 A  Front and middle wheel: currents similar as above  Problems in climbing phase can be detected (oscillation of signal) wheels slipping wheel slipping - lack of grip

23. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 23 Example: Rover Comparison – Simulation vs. Experiments  Qualitative Analysis  Strong correlation predictions – measurements  Significantly higher torque (SR = 0 %, 2.5 A) and friction coefficient (SR = 47 %, 3.5 A) of RCL-E than CRAB (SR = 100 %, 2.5 A)  Same ranking simulation/hardware for all metrics  Quantitative Analysis  Discrepancy of numerical values (~40 %)  Static, ideal model  Validation of simulations through hardware tests (Ref: Thueer, Krebs, Lamon & Siegwart, JFR Special Issue on Space Robotics, 3/2007)

24. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 24 Challenging Environment on Mars  Spirit and Opportunity Robots on Mars – since 24.1.2004

25. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 25 Motion Control – Tactile Wheels  Improvement of locomotion performance through motion control  Control types  Torque control  Kinematics based velocity control  Need for tactile wheel  Wheel ground contact angle required  First prototype on Octopus  Development of new “metallic“ wheel

26. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 26 Flexible Wheels  Better tractive performance  Lower total motion resistance Total sinkage [mm] Wheel deflection [mm] Max. soil slope [°] Required wheel output torque [Nm] Combined output power (6 wheels) [W] Required input power [W] Rigid wheel D=35 cm, b=15 cm, grouser height=3.4 cm, i=10 % 45.8-13.913.8710.625.2 Flexible wheel D=35 cm, b=15 cm, grouser height=0.1 cm, pressure on rigid ground=5 kPa, i=10 % 12.912.813.96.174.711.2 Courtesy of DLR Köln

27. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 27 Navigation – Motion Estimation and Control in Rough Terrain

28. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 28 Conclusion  Locomotion mechanisms and their characteristics  Metrics for different aspects of performance  Example of evaluation and comparison of systems  Focus on obstacle negotiation aspect of locomotion performance  Static 2D analysis in simulation  Verfication and validation with hardware  How to improve performance  Motion control  Tactile wheel as sensor for wheel ground contact angle

29. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 29 Outlook  Continuous Flight on Mars 3.2 m

30. © Autonomous Systems Lab, ETH Zurich Autonomous Systems Lab 30 Thanks for your attention!  Acknowledgement  This work was partially supported through the ESA ExoMars Program and conducted in collaboration with Oerlikon Space, DLR and vH&S Questions ?