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Kinematics

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The branch of mechanics concerned with the motions of objects without regard to ... Revolute Joints. 3 DOF joint. gimbal. ball and socket. 2 DOF joint. universal ... – PowerPoint PPT presentation

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Title: Kinematics


1
Kinematics
2
Kinematics
  • The branch of mechanics concerned with the
    motions of objects without regard to the forces
    that cause the motion
  • Why kinematics?
  • Hierarchical articulated model
  • Posing a character

3
Degrees of Freedom (DOF)
  • The minimum number of coordinates required to
    specify completely the motion of an object

yaw
roll
pitch
6 DOF x, y, z, raw, pitch, yaw
4
Degrees of Freedom in Human Model
  • Root 3 translational DOF 3 rotational DOF
  • Rotational joints are commonly used
  • Each joint can have up to 3 DOF
  • Shoulder 3 DOF
  • Wrist 2 DOF
  • Knee 1 DOF

3 DOF
1 DOF
5
Revolute Joints
  • 3 DOF joint
  • gimbal
  • ball and socket
  • 2 DOF joint
  • universal

6
Hierarchical Articulated Model
  • Represent an articulated figure as a series of
    links connected by joints
  • Enforce limb connectivity in a tree-like
    structure

root
7
Joint Space vs. Cartesian Space
  • Joint space
  • space formed by joint angles
  • position all jointsfine level control
  • Cartesian space
  • 3D space
  • specify environment interactions

8
Forward and Inverse Kinematics
  • Forward kinematics
  • mapping from joint space to cartesian space
  • Inverse kinematics
  • mapping from cartesian space to joint space

Forward Kinematics
Inverse Kinematics
9
Forward and Inverse Kinematics (cont.)
  • Forward kinematics
  • rendering
  • Inverse kinematics
  • good for specifying environment interaction
  • good for controlling a characterfewer parameters

10
Forward Kinematics
?
End Effector
Base
11
Forward Kinematics by Composing Transformations
End Effector
Relative rotation from the local coordinate of
parent link to the local coordinate of the child
link
12
Tree Traversal
M I
T0
M T0
T1.1
M T0T1.1
T 1.2
M T0T1.1T2.1
T2.1
T2.2
M T0T1.1
M T0
M T0T1.2
M T0T1.2T2.2
13
Inverse Kinematics
End EffectorP
Base
14
Redundancy in IK
  • Our example
  • 2 equations (constraints)
  • 3 unknowns
  • Multiple solutions exist!
  • This is not uncommon!
  • see how you can move your elbow while keeping
    your finger touching your nose

15
Other problems in IK
  • Infinite solutions

16
Other problems in IK
  • No solutions

17
Why is IK hard?
  • Redundancy
  • Natural motion
  • joint limits
  • minimum jerk
  • style?
  • Singularities
  • ill-conditioned matrix
  • shown later

18
Solving Inverse Kinematics
  • analytic method
  • inverse-Jacobian method
  • optimization-based method
  • example-based method

19
Analytic Method
q2
L2
Goal
L1
(X,Y)
q1
20
Analytic Method
L2
180- q2
L1
(X,Y)
q1
Y
qT
X
21
Cosine Law
C
A
a
B
22
Analytic Method
L2
(X,Y)
L1
180- q2
q1
qT
Y
X
23
Inverse-Jacobian method
  • When linkage is complicated
  • Iteratively change the joint angles to approach
    the goal position and orientation

24
Jacobian
  • Jacobian is the n by m matrix relating
    differential changes of q to differential
    changes of P (dP)
  • Jacobian maps velocities in joint space to
    velocities in cartesian space

25
An Example of Jacobian Matrix
26
Iteratively Solving
P
PdP
Pgoal
  • Linearize about qk locally
  • Small increments

27
Jacobian may not be invertible!
  • Non-square matrix
  • pseudo inverse
  • Singularity
  • causes infinite joint velocities
  • occurs when any cannot achieve a given

28
Pseudo Inverse of the Jacobian
29
Null space
  • The null space of J is the set of vectors which
    have no influence on the constraints
  • The pseudoinverse provides an operator which
    projects any vector to the null space of J

30
But it can be used to bias The solution vector
31
Utility of Null Space
  • The null space can be used to reach secondary
    goals
  • Or to find natural pose / control stiffness of
    joints

32
Optimization-based Method
  • Formulate IK as an nonlinear optimization problem
  • Example
  • Objective function
  • Constraint
  • Iterative algorithm
  • Nonlinear programming method by Zhao Badler,
    TOG 1994

33
Objective Function
  • distance from the end effector to the goal
    position/orientation
  • Function of joint angles G(q)

34
Objective Function
Position Goal
Orientation Goal
Position/Orientation Goal
weighted sum
35
Nonlinear Optimization
  • Constrained nonlinear optimization problem
  • Solution
  • standard numerical techniques
  • MATLAB or other optimization packages
  • usually a local minimum
  • depends on initial condition

limb coordination
joint limits
36
Example-based Method
  • Utilize motion database to assist IK solving
  • IK using interpolation
  • Rose et al., Artist-directed IK using radial
    basis function interpolation, Eurographics01

37
Example-based Method (cont.)
  • IK using constructed statistical model
  • Grochow et al., Style-based inverse kinematics,
    SIGGRAPH04
  • Provide the most likely pose based on given
    constraints

38
Videos
  • Style-based inverse kinematics

39
Assignment 1
  • Keyframing using different interpolation
    approaches and orientation representations
  • Due at 1159 PM on Oct 15 (Sun)
  • An ASF/AMC viewer is provided
  • No need to be panic on FK implementation!
  • See the course web for details
  • Quick tutorial given by TA on Thursday

40
Notes on ASF/AMC format
  • Developed by Acclaim
  • ASF
  • skeleton file
  • defines tree hierarchy
  • defines rotation axes of a joint
  • represented in global coordinate
  • defines bone length
  • AMC
  • motion file
  • Euler angles

41
Example Results
42
Forward Kinematics
A series of transformations on an object can be
applied as a series of matrix multiplications
position in the global coordinate
position in the local coordinate
43
Hierarchical Articulated Model
  • Represents a characters skeleton as a series of
    links connected by joints
  • connectivity constraints is enforced in a
    tree-like structure
  • family of parent-child spatial relationships
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