Trajectory planning is a fundamental problem for industrial robots. It is particularly challenging for robot manipulators that transfer silicon wafers in an equipment front end module (EFEM) of a semiconductor manufacturing machine where the work space is extremely limited. Existing methods cannot give satisfactory performance since they usually solve the problem partially. Motivated by this demand in industrial applications and to solve all aspects of the problem, this paper proposes to learn the work environment beforehand by probabilistic roadmap (PRM) method for collision avoidance. The cycle time preference and the robot kinematic hard constraints are considered properly. A constrained optimization problem is formulated with the shortest path searched from the roadmap and parametrized by a cubic B-spline curve, which simplifies the optimization process.

References

References
1.
Hsu
,
D.
,
Sanchez-Ante
,
G.
, and
Sun
,
Z.
,
2005
, “
Hybrid PRM Sampling With a Cost-Sensitive Adaptive Strategy
,”
Proceedings of the IEEE International Conference on Robotics and Automation
, pp. 3874–3880.
2.
Kavralu
,
L. E.
,
Svestka
,
P.
,
Latombe
,
J.-C.
, and
Overmars
,
M. H.
,
1996
, “
Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces
,”
IEEE Trans. Rob. Autom.
,
12
(
4
), pp. 566–580.
3.
Kavraki
,
L. E.
,
Kolountzakis
,
M. N.
, and
Latombe
,
J.-C.
,
1998
, “
Analysis of Probabilistic Roadmaps for Path Planning
,”
IEEE Trans. Rob. Autom.
,
14
(
1
), pp. 166–171.
4.
Ratliff
,
N.
,
Zucker
,
M.
,
Bagnell
,
J. A.
, and
Srinivasa
,
S.
,
2009
, “
CHOMP: Gradient Optimization Techniques for Efficient Motion Planning
,”
IEEE International Conference on Robotics and Automation
, pp. 489–494.
5.
Park
,
C.
,
Pan
,
J.
, and
Manocha
,
D.
,
2012
, “
ITOMP: Incremental Trajectory Optimization for Real-Time Replanning in Dynamic Environments
,”
International Conference on Automated Planning and Scheduling
, Sao Paulo, Brazil, June 25–29.
6.
Hauser
,
K.
, and
Ng-Thow-Hing
,
V.
,
2010
, “
Fast Smoothing of Manipulator Trajectories Using Optimal Bounded-Acceleration Shortcuts
,”
IEEE International Conference on Robotics and Automation
, pp. 2493–2498.
7.
Gasparetto
,
A.
, and
Zanotto
,
V.
,
2007
, “
A New Method for Smooth Trajectory Planning of Robot Manipulators
,”
Mech. Mach. Theory
,
42
(
4
), pp.
455
471
.
8.
Wang
,
C.-H.
, and
Horng
,
J.-G.
,
1990
, “
Constrained Minimum-Time Path Planning for Robot Manipulators Via Virtual Knots of the Cubic B-Spline Functions
,”
IEEE Trans. Autom. Control
,
35
(
5
), pp. 573–577.
9.
Lepetič
,
M.
,
Klančar
,
G.
,
Škrjanc
,
I.
,
Matko
,
D.
, and
Potočnik
,
B.
,
2003
, “
Time Optimal Path Planning Considering Acceleration Limits
,”
Rob. Auton. Syst.
,
45
(
3–4
), pp.
199
210
.
10.
Judd
,
K. B.
, and
McLain
,
T. W.
,
2001
, “
Spline Based Path Planning for Unmanned Air Vehicles
,” AIAA
Guidance, Navigation, and Control Conference and Exhibit, Guidance, Navigation, and Control and Co-located Conferences
, Montreal, QB, Canada, Aug. 6–9.
11.
Cao
,
B.
,
Doods
,
G. I.
, and
Irwin
,
G. W.
,
1994
, “
Time-Optimal and Smooth Constrained Path Planning for Robot Manipulators
,”
IEEE International Conference on Robotics and Automation
, Vol. 3, pp. 1853–1858.
12.
LaValle
,
S. M.
, 2006,
Planning Algorithms
, J. O'Kane, ed.,
Cambridge University Press
, New York.
13.
Bobrow
,
J. E.
,
Dubowsky
,
S.
, and
Gibson
,
J. S.
,
1985
, “
Time-Optimal Control of Robotic Manipulators Along Specified Paths
,”
Int. J. Rob. Res.
,
4
(
3
), pp. 3–17.
14.
Chen
,
Y.-C.
,
1991
, “
Solving Robot Trajectory Planning Problems With Uniform Cubic B-Splines
,”
Optim. Control Appl. Methods
,
12
(
4
), pp.
247
262
.
15.
Zanotto
,
V.
,
Gasparetto
,
A.
,
Lanzutti
,
A.
,
Boscariol
,
P.
, and
Vidoni
,
R.
,
2011
, “
Experimental Validation of Minimum Time-Jerk Algorithms for Industrial Robots
,”
J. Intell. Rob. Syst.
,
64
(
2
), pp.
197
219
.
16.
Choset
,
H.
,
Lynch
,
K.
,
Hutchinson
,
S.
,
Kantor
,
G.
,
Burgard
,
W.
,
Kavraki
,
L.
, and
Thrun
,
S.
, 2005,
Principles of Robot Motion: Theory, Algorithms, and Implementations (Intelligent Robotics and Autonomous Agents series)
,
A Bradford Book, The MIT Press
,
Cambridge, MA
.
17.
Kim
,
J.-T.
, and
Kim
,
D.
,
2014
, “
Manipulator Motion Planning With Unconstrained End effector Under Obstacle Environment
,”
Adv. Rob.
,
28
(
8
), pp.
533
544
.
18.
Hwang
,
Y. K.
,
Watterberg
,
P. A.
,
Chen
,
P. C.
, and
Lewis
,
C. L.
,
1991
,
General Techniques for Constrained Motion Planning
,
Educational Testing Service
,
Princeton, NJ
.
19.
Capisani
,
L. M.
,
Facchinetti
,
T.
,
Ferrara
,
A.
, and
Martinelli
,
A.
,
2013
, “
Obstacle Modelling Oriented to Safe Motion Planning and Control for Planar Rigid Robot Manipulators
,”
J. Intell. Rob. Syst.
,
71
(
2
), pp.
159
178
.
20.
Gasparetto
,
A.
, and
Zanotto
,
V.
,
2008
, “
A Technique for Time-Jerk Optimal Planning of Robot Trajectories
,”
Rob. Comput.-Integr. Manuf.
,
24
(
3
), pp.
415
426
.
21.
Luenberger
,
D. G.
, and
Ye
,
Y.
, 2008,
Linear and Nonlinear Programming
,
3rd ed.
,
Springer-Verlag
, US, New York.
22.
Larsen
,
E.
,
Gottschalk
,
S.
,
Lin
,
M. C.
, and
Manocha
,
D.
, 1999, “
Fast Proximity Queries With Swept Sphere Volumes
,” Department of Computer Science, UNC Chapel Hill,
Technical Report No. TR99-018
.
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