Abstract: Caging is a method to capture an object geometrically by position-controlled robots without any force and tactile sensors. Many previous researches focused on caging constraints of objects, and those on planning are few. In this paper, we present a motion planner for caging by a multifingered hand and a manipulator to produce whole motion which includes approaching to a target object and capturing it without any collisions. We derive sufficient conditions required for the caging tasks about three caging patterns. Since the planner requires the object properties including the position and orientation of the object, we adopt an object recognition using AR picture markers. We apply the proposed method to caging about four target objects: a cylinder, a ring, a mug and a dumbbell. Some experimental results shows that each motion are successfully planned, and executed by the arm/hand system. Presented in ICMA2012 in Chengdu, China

1. S. Makita (Sasebo National College of Tech.)
K. Okita (Canon Inc.)
Y. Maeda (Yokohama National University)
ICMA 2012 in Chengdu, Sichuan, China, Aug.5-8, 2012
WP2-7 Algorithm #232781

2.  3D Multifingered caging
◦ Geometrical constraint
 No force sensor and
control
◦ By position-controlled
robot
◦ Only geometrical
information of objects
are required
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3.  Caging a concave object by 2
fingertips in 2D [Rimon1999]
◦ Not in 3D space
 Caging
an object by pointed finger in
n-dimension [Pipattanasomporn2007]
◦ Not practical hands
 Caginggrasps by a humanoid robot
[Diankov2008]
◦ Caging conditions were not derived
3

4.  Caging for some simple-shaped
objects by a practical robot hand
[Makita2008]
◦ Theories to confine the target object
◦ RRT-based planning of finger
configuration
4

5.  Automatic caging system
◦ Planning caging motions of arm/hand
robot
◦ Object recognition using AR picture
marker
How to cage
the object…
5

6. 1. Classify the patterns of caging to
determine strategies
2. Motion planning by RRT (Rapidly-
exploring Random Trees [Lavalle])
3. Biased Sampling by solving inverse
kinematics of the robot arm
4. Object recognition using AR picture
marker
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7. 7

8.  Envelope-type Caging
◦ A robot hand surrounds the object.
8

9.  Ring-type Caging
◦ The fingers of the hand are inserted to
the hollow of the object.
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10.  Waist-type Caging
◦ The fingers are wound around the
constricted part of the object.
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11. Both fingertips are
closer than the
thickness of the object
The finger goes
through the hollow
region of the object
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12. The constricted part
cannot escape from the
gap between fingertips
The constricted part
goes through the hollow
region of the hand
The disk-shaped part
cannot escape from the
ring-formed hand
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13. 13

14.  Make configuration path
Satisfying
sufficient
conditions
Random sampling
Collision
check
Initial
configuration
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15. Randomly-sampled
configuration
Biased sampling
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16. Lhand:
Presumption of the length of
the hand
1. Give a desired position of the tool
center point of the manipulator.
2. Give a desired orientation randomly.
3. Solve the inverse kinematics
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17.  Necessary info. for planning
◦ Category of objects
◦ Size
◦ Posture
17

18. Camera
Object
18

19. 19

20.  6-DOF manipulator
(Yaskawa MOTOMAN-HP3J)
 Hand: 2 fingers and 2 joints for each
20

21. 50 124 50
38
124
16 8
50 150
cylinder ring mug
21

22. 1
26
45
222
22

23.  Caging a mug
23

24. 24

25.  Some sufficient conditions for caging
are derived
 Motion planning for caging by a robot
arm/hand system can be succeeded
for four objects.
 Biased-sampling by solving IK
contributes to searching efficiency.
 Object recognition by using AR
marker is presented.
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26.  Reducing planning time
 Object recognition only by cameras
 More variations on caging patterns
◦ Especially, envelope-type caging
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