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
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Motion Planning for 3D Multifingered Caging with Object Recognition using AR Picture Markers / Icma2012 presentation
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
2
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
6
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.
9
10. Waist-type Caging
◦ The fingers are wound around the
constricted part of the object.
10
11. Both fingertips are
closer than the
thickness of the object
The finger goes
through the hollow
region of the object
11
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
12
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
16
17. Necessary info. for planning
◦ Category of objects
◦ Size
◦ Posture
17
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.
25
26. Reducing planning time
Object recognition only by cameras
More variations on caging patterns
◦ Especially, envelope-type caging
26
