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May 2012 1
Motion Planning
Shmuel WimerBar Ilan Univ., Eng. Faculty
Technion, EE Faculty
May 2012 2
Outline
• Problem definition
• Point robot
• Work space and configuration space
• Minkowski sums
• Translational motion planning
• Rotational motion planning
May 2012 3
Types of Robots and Motions
Articulated Robot
Translation Motion
start goal
May 2012 4
Rotational Motion
start goal
Some robots can move in any direction (e.g., ants)
Some robots cannot translate (e.g., cars)
We’ll study translational and rotational motions
Given a robot, is there a free paths (no collisions)
from start to goal?
May 2012 5
Work Space and Configuration Space
2
1
A robot is moving in ,called ,
consisting of a set , , of obstacles.tS
R
P P
work space
Reference Point
0,0R
9,7R
2 Degrees of freedom 3 Degrees of freedom
0,0,0R
9,7,45R
45
May 2012 6
The parameter space of a robot is called its
, denoted . A point
corresponds to a certain placement
of the robot in the work space.
p
p
R
C R
C R
R
configuration space
Configuration spaceWork space
May 2012 7
Free Space Computation
Divide into trapezoids.
It takes O(nlogn)
expected time.
Remove trapezoids of obstacle in O(n) time.
May 2012 8
Building a Road Map
Allocate node at center of vertical edges
Allocate node at center of every trapezoid
Connect center nodes to edge nodes
Done in O(n) with doubly-connected edge list
May 2012 9
Computing a Path
Get from start to center of trapezoid in O(logn) time
Get from goal to center of trapezoid in O(logn) time
Connect center of trapezoids by BFS in O(n) time
startp
goalp
May 2012 10
Let be a point robot moving among a
set of polygonal obstacles with edges in total.
can be processed in log expected time, such
that between any start and goal position a collision-
fr
S n S
O n n
: RTheorem
ee path for can be computed in time, if it
exists.
O nR
May 2012 11
Convert a problem with polygonal robot into point robot
by modifying the obstacles in the configuration space to
incorporate the geometry of the robot.
Minkowski Sums
obstacle
robot
May 2012 12
- convex obstacle, , - convex robot x yP R
, | ,
configuration space obstacle
x y x y P R P
2 21 2 1 2
1 2 1 2
, . -
| ,
S S S S
S S p q p S q S
R R Minkowski sum
, , , , ,x y x y x x y yp p p q q q p q p q p q
Reflection of a set about the origin
, , | x yp p p S p p S
May 2012 13
Let be a planar translating robot and
let be an obstacle. Then 0,0 .
:Theorem R
P P P R
We'll show:
, , 0,0x y x y
:
Proof
R P P R
Let , ,
, ,
, 0,0
, 0,0 .
x y
x y
x y
x y
q q q x y
q q q x y
q x q y
q x q y
R P
R
R
R
May 2012 14
But since , also,
, , ,
, 0,0 .
x y
x y x y
q q q
x y q q q x q y
x y
P
P R
Converesely, let , 0,0 . Then
there are points , and , 0,0
such that , ,
, , ,
x y x y
x x y y
x y x y
x y
p p r r
x y p r p r
x r y r p p x y
P R
P R
R P ■
May 2012 15
Extreme Points and Directions
p rP R
r
R
P
p
An extreme point of in direction
is the sum of extreme points of and in .d d
:����������������������������Observation P R
P R
May 2012 16
When a vertex of interacts (slides)
with an edge of , the direction of the resulting edge
in is that of the edge of .
:Observation R
P
P R P
How complex is Minkowski sum of two polygons?
Let and be two -vertex and
-vertex convex polygons, respectively.
is convex and has edges at most.
n
m
n m
:Theorem R P
P R
Convexity follows directly by definition
of Minkowski sum and convexity of and .
:Proof
R P
May 2012 17
is extreme in the direction of its outer
normal, hence must be defined by extreme points
in and in that direction.
e P R
P R
At least one of and must have an edge in that
direction. We charge that edge to and it will not
be charged again.
e
P R
■
May 2012 18
-R
a
b
cd
-R
a
b
cd
-R
a
b
cd
-R
a
b
cd
-R
a
b
cd
-R
a
b
cd
Construction of Minkowski Sum
1
2
3 4
5P
Edges of P and R are labeled counterclockwise
b
1
2
34
5
c
d
d
c
aa
b
May 2012 19
The self-intersecting polygonal path bounding
is labled, including edges near reflex vertices of .
The pattern of lables along can be derived from
a of the edge vectors of and .
P
P
P Rstar diagram
1
2
3
45
a
b
c
d
1
2
3 4
5P
-R
a
b
cd
May 2012 20
Starting at 1, circle around the star counterclockwise.
Between and 1 of , write the encountered lables
of .
i i
P
Rb
1
2
34
5
c
d
d
c
aa
b
1
2
3
45
a
b
c
d
May 2012 21
2 2
convex convex
convex nonconvex log
nonconvex nonconvex log
O n O n
O n O n n
O n O n n
Size of Time ComplexityR P P
Let have vertices and have fixed
(constant) number of vertices. Then
can be constructed in the following complexities:
n
:Theorem P R
P P R
May 2012 22
Rotation – Moving a Ladder
Minkowski sum for
ladder at 0º rotation.
Blockage exists.
May 2012 23
Minkowski sum for
ladder at 30º rotation.
Blockage exists.
Minkowski sum for
ladder at 60º rotation.
Blockage changed.
May 2012 24
Conversion to 3D Motion Problem
Bottom viewFront view. θ varies from 0º to 75º.
Ladder’s reference point can move in the 3D space!
May 2012 25
Cell Decomposition
Minkowski sums
A
B
C
Cell decomposition
108
9
2 1
3
4
56
7
Obstacles
∞
R
Ladder
May 2012 26
• A Cell is the collection of all free points labeled
with the same front/back edge label pairs.
– A: (3,2); B: (3,8); C: (1,9)
• Cell decomposition has discontinuities when
ladder is oriented similar to an edge.
• There are finite number of ladder rotation where
cell decomposition is changing.
– New cells can appear and old ones may disappear.
May 2012 27
56
7
108
9
2 1
3
4
∞
R
AB
C disappeared
Ladder is rotated
May 2012 28
In Connectivity Graph Gθ nodes are cells of decomposition
and edges are connecting nodes corresponding to adjacent
cells in free area (a kind of dual graph).
A: (3,2)B: (3,8)
(1,8)
C: (1,9)(1,∞)
(10,∞)
(5,∞)
(3,∞)
G0º
May 2012 29
(4,∞)
(1,∞)
(10,∞)
(5,∞)
(3,∞)
A: (3,2)B: (3,8)
(1,8)
(7,8)
Critical Orientations correspond to slants of edges.
May 2012 30
Connectivity Graph G is constructed by stacking the
connectivity graphs Gθ corresponding to the critical
orientations.
Vertices of two distinct Gθ are connected iff they are
labeled with the same edge pair. Starting from G0, Gθ
are added in increasing order of θ, thus creating a
layered 3D graph.
A paths from start to goal if exists can be found by a
BFS algorithm.
May 2012 31
5
2 *
2
2
2
2
Schwatz & Sharir 1983
O'Dunlaing et. al. 1987 log log
logLeven & Sharir 1987
logSifroni & Sharir 1987
Vegter 1990
O'Rourke 1985
O n
O n n n
O n n
O n n
O n
n
Time ComplexityAuthors
History