CS 231. Control for articulate rigid-body dynamic simulation. Articulated rigid-body dynamics

Transcription

1 CS 231 Control for articulate rigid-body dynamic simulation Articulated rigid-body dynamics F = ma No control 1

2 No control Ragdoll effects, joint limits RT Speed: many sims at real-time rates on today s computers With control control, F dynamic model integrator state graphics 2

3 Control laws where do they come from? Biomechanics Robotics Observation/ Intuition Physical principles Optimization 3

4 Control laws where do they come from? Biomechanics limited Robotics (Hsai,, 99) Observation/ Intuition Physical principles Optimization Control laws where do they come from? Biomechanics limited Robotics Observation/ Intuition Physical principles Optimization 4

5 Control laws where do they come from? Biomechanics limited Robotics Observation/ Intuition Physical principles Optimization Control laws where do they come from? Biomechanics limited Robotics Observation/ Intuition Physical principles Optimization 5

6 Control laws where do they come from? Biomechanics limited Robotics Observation/ Intuition Physical principles Optimization Deliberate, directed control Hopping Control - a case study 3 rigid links 2 controlled dof L 6

7 Hopping Controller drawn from robotics Hierarchy of control: state machine control actions torque/forces computed Hopping Controller state machine 7

8 Hopping Controller control actions Hopping Controller Force computed Forces bring the length of the leg to the desired length during flight plus add energy during thrust 8

9 Hopping Controller control actions Velocity controlled by foot placement x = v T s /2 + k v (v - v d ) where T s is time of stance This displacement is added to the current x to find position of desired touchdown Hopping Controller Torque computed 9

10 Running Controller two legged biped Running Controller idle leg control 10

11 Video Break Video- On the run Quadruped Controller four legged control Bound Trot Gallop Pronk 11

12 SIMBICON: Simple Biped Locomotion Control Creating humanoid simulations using dynamics SIMBICON: Simple Biped Locomotion Control 12

13 SIMBICON: Simple Biped Locomotion Control Control torso and swing-hip wrt world frame COM feedback v v COM velocity d<0 d>0 COM position 13

14 COM feedback Base controller Continuous feedback GUI 14

15 Apply the same control ideas to both sagittal and coronal planes for 3D Video Break 15

16 Other controls diving and gymnastics Other controls diving and gymnastics 16

17 Building more complex behaviors from simple behaviors Wooten (2000) combines: leaping, tumbling, landing, balancing Can this be done automatically? Building more complex behaviors from simple behaviors Faloutsos et al Build behaviors walk, sit, stand, fall Set-up transitions between behaviors Learn pre-conditions for each controller's success or failure and classify them Supervisor controller swaps between when conditions for new control are met 17

18 Building more complex behaviors from simple behaviors Faloutsos et al

19 Adapting control to new characters Adapting control geometric scaling 19

20 Adapting control mass scaling Adapting control to new characters 20

21 Combining motion capture and control for human characters Human motion capture rich with style, detail hard to adapt to new characters & scenarios Dynamic simulation physically realistic handles a changing environment requires controller Combining motion capture for control Motion capture Libraries of examples 21

22 Tracking Control Converted Angle Data desired joint angles Tracking Control computed torques Dynamic Model Tracking Control Converted Angle Data Tracking Control Dynamic Model PD-servo controller computes torques = = k( d ) b( d from motion data k and b are uniform stiffness and damping Note: No joint limits, influenced by data 22

23 Tracking Control Converted Angle Data Tracking Control Dynamic Model Inertia scaling for stiffness and damping k and b are scaled by moment of inertia: k = k' * MOI b = b' * MOI MOI effect MOI effect tune for uniform k and b high stiffness + moderate damping = good tracking 23

24 Control: for reacting to contact Dynamic impact information is not recorded Apply reaction forces Collision handler hierarchical detection penalty force reaction 24

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26 System Layout Balance Control 26

27 Balance Control Offset method Angle offsets computed: Angle offsets applied: Balance Control Virtual Actuator Inspired by Pratt (1995) External force controls center of mass: 27

28 Balance Control Virtual Actuator Convert force to torque as virtual actuator: 28

29 Lower-body Control Balanced standing Controller's goal: Keep the simulation's center of mass (com) safely inside the support polygon made by the feet To accomplish the goal: Pick a desired com and minimize errors by making corrections in the leg actuation 29

30 Control: for reacting to contact Control: for reacting to contact React to forces Recover smoothly 30

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32 Control: for boxing Quantitative evaluation 32

33 Progress Reports due next class, in class Intro from proposal Scope from proposal Methods *new Challenges/to do s s *revised Findings *new 33