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Zürich Autonomous Systems Lab Cedric Pradalier ICRA Workshop on Planetary Rovers, May 2010.

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Presentation on theme: "Zürich Autonomous Systems Lab Cedric Pradalier ICRA Workshop on Planetary Rovers, May 2010."— Presentation transcript:

1 Zürich Autonomous Systems Lab Cedric Pradalier ICRA Workshop on Planetary Rovers, May 2010

2 Zürich Autonomous Systems Lab 2 Welcome to Anchorage

3 Zürich Autonomous Systems Lab 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 whats come ahead

4 Zürich Autonomous Systems Lab 4 Lab of Pr. Siegwart 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

5 Zürich Autonomous Systems Lab Overview, Crab, Eurobot EGP Prototype Exomars Breadboard

6 Zürich Autonomous Systems Lab 6 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

7 Zürich Autonomous Systems Lab 7 Nanokhod Shrimp & Solero Passive suspension systems 6 motorized wheels 2 steering Very good terrainability! ASL rovers background

8 Zürich Autonomous Systems Lab 8 RCL-E RCL-C CRAB Exomars: Pre-study phase A

9 Zürich Autonomous Systems Lab 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 Zürich Autonomous Systems Lab 10 ExoMars Breadboard

11 Zürich Autonomous Systems Lab 11 ExoMars Breadboard

12 Zürich Autonomous Systems Lab 12 Authorization denied… Test plan and results

13 Zürich Autonomous Systems Lab 13 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. IROS07

14 Zürich Autonomous Systems Lab 14 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

15 Zürich Autonomous Systems Lab 15 Mechanical design

16 Zürich Autonomous Systems Lab 16 Mechanical design

17 Zürich Autonomous Systems Lab 17 Implementation

18 Zürich Autonomous Systems Lab 18 Suspension

19 Zürich Autonomous Systems Lab 19 Integration 880kg, without astronaut…

20 Zürich Autonomous Systems Lab 20 Integration

21 Zürich Autonomous Systems Lab Optimised Torque Control Learning what comes ahead

22 Zürich Autonomous Systems Lab 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 IROS10

23 Zürich Autonomous Systems Lab 23 Control loop

24 Zürich Autonomous Systems Lab 24 Test setup and hardware

25 Zürich Autonomous Systems Lab 25 Results

26 Zürich Autonomous Systems Lab 26 Results

27 Zürich Autonomous Systems Lab Ambroise Krebs

28 Zürich Autonomous Systems Lab 28 Objectives Online learning Obtain the terrain description while operating No prior regarding the terrains to encounter No predefined number of classes to learn No trained classifiers Adaptive navigation Path optimized according to a Rover-Terrain Interaction metric => Trafficability & Terrainability The metric makes use of the knowledge acquired Control the rover to follow the path planned End-to-end and integrated approach Robotic platform: CRAB

29 Zürich Autonomous Systems Lab 29 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 ?

30 Zürich Autonomous Systems Lab 30 Delay Approach: Architecture overview RTILERover-Terrain Interactions Learned from Experiments SOFTWARE HARDWAREActuators Controller Path Planning Prediction Learning Database ProBT Near to far Local Sensors Remote Sensors Obst. Det. Trafficability & Terrainability Traversability

31 Zürich Autonomous Systems Lab 31 Outline SOFTWARE HARDWAREActuators Controller Path Planning Prediction Learning Database ProBT Near to far Delay Local Sensors Remote Sensors Obst. Det.

32 Zürich Autonomous Systems Lab 32 Data acquisition: 2D example Grid based approach RemoteImage acquisition LocalPosition of the wheels SamplesWhen learning occurs Near to far Samples can be used for the learning mechanism. Remote Local Features association

33 Zürich Autonomous Systems Lab 33 Bayesian model Goal Local features predicted based on remote features Bayesian model Joint distribution and decomposition Introduce abstraction classes and Question Class associationLocal classificationRemote classification

34 Zürich Autonomous Systems Lab 34 Local and remote distributions Each class represented with Gaussian model Additional « unknown » class with a uniform distribution Example 2 classes (C=2) Class 0 = "unknown" Learning mechanism F P(F | K) K=0 K=1 K=2

35 Zürich Autonomous Systems Lab 35 Learning mechanism Novelty detection Uniform distribution acts as a threshold. F P(F | K)

36 Zürich Autonomous Systems Lab 36 Remote Learning mechanism Class association Probabilistic table filled using the samples information Local

37 Zürich Autonomous Systems Lab 37 Outline SOFTWARE HARDWAREActuators Controller Path Planning Prediction Learning Database ProBT Near to far Delay Local Sensors Remote Sensors Obst. Det.

38 Zürich Autonomous Systems Lab 38 Prediction Process Remote SubspaceLocal Subspace F r = 0.5 Prediction 20%50%30%

39 Zürich Autonomous Systems Lab 39 Path planner – E* Wavefront propagation Navigation function Gradient descent Propagation cost Process Adaptive navigation assumption T = 1 Image acquisitionF l predictionPropagation costs

40 Zürich Autonomous Systems Lab 40 Outline

41 Zürich Autonomous Systems Lab 41 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 StartGoal ?

42 Zürich Autonomous Systems Lab 42 RTILE: Results Adaptive navigation Test environment in Fluntern 3 terrains Grasssoftest(best) Tartan Asphalthardest (worst) Automatically driven 6 cm/s No prior Learning every 6 m

43 Zürich Autonomous Systems Lab 43 RTILE: Results complete Test of the complete approach Waypointsx [m]y [m]

44 Zürich Autonomous Systems Lab 44 RTILE: Results complete Test parameters Quantitative Results LearningPredictionCamera# Runs CαCα CβCβ horizonRTILEDefault Every 6 m0.445 m22

45 Zürich Autonomous Systems Lab 45 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 M RTI 20% M RTI improvement 10% longer distance Terrain description Consistent interaction with E* Dynamical adaptation of the propagation costs RTILE improves the rover behavior

46 Zürich Autonomous Systems Lab 46 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

47 Zürich Autonomous Systems Lab 47 Questions?


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