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Research Papers

Trajectory Planning for Robot Manipulators Considering Kinematic Constraints Using Probabilistic Roadmap Approach

[+] Author and Article Information
Xiaowen Yu

Mechanical System Control Laboratory,
Department of Mechanical Engineering,
University of California,
Berkeley, CA 94720
e-mail: aliceyu@berkeley.edu

Yu Zhao

Mechanical System Control Laboratory,
Department of Mechanical Engineering,
University of California,
Berkeley, CA 94720
e-mail: yzhao334@berkeley.edu

Cong Wang

Assistant Professor
Department of Electrical and Computer Engineering,
New Jersey Institute of Technology,
Newark, NJ 07102
e-mail: cong.wang@njit.edu

Masayoshi Tomizuka

Professor
Department of Mechanical Engineering,
University of California,
Berkeley, CA 94720
e-mail: tomizuka@berkeley.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received February 24, 2016; final manuscript received August 26, 2016; published online October 18, 2016. Assoc. Editor: Davide Spinello.

J. Dyn. Sys., Meas., Control 139(2), 021001 (Oct 18, 2016) (8 pages) Paper No: DS-16-1118; doi: 10.1115/1.4034748 History: Received February 24, 2016; Revised August 26, 2016

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.

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References

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Figures

Grahic Jump Location
Fig. 4

The EFEM is modeled as a closed boundary obstacle: (a) obstacle-free space and (b) samples generated by PRM (1500 nodes and 39,900 edges, edges are not displayed)

Grahic Jump Location
Fig. 3

Motion is considered inside the EFEM

Grahic Jump Location
Fig. 2

Model of the three-axis manipulator works in an EFEM: (a) an initial robot configuration inside the EFEM and (b) a goal robot configuration inside the EFEM

Grahic Jump Location
Fig. 7

Bounded region of a given piecewise linear path

Grahic Jump Location
Fig. 5

Obstacle-free space and samples generated by PRM

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Fig. 6

The shortest path connects qI and qG searched in roadmap by A*: (a) bounding boxes for all connecting points of a path and (b) bounding box for a single connecting point

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Fig. 1

A robot manipulator works inside an EFEM of a semiconductor manufacturing machine

Grahic Jump Location
Fig. 8

One link robot example for determining ci

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Fig. 9

Control points' distribution example: (a) trajectory with time sequence substituted and (b) zoom in of a segment of the trajectory

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Fig. 10

Trajectory generated by B-spline curve

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Fig. 11

The motion sequence of the trajectory: (a) position of the three joints for the optimized trajectory and (b) velocity of the three joints for the optimized trajectory

Grahic Jump Location
Fig. 12

Position and velocity of the three joints for the optimized trajectory (under constraints in Table 2): (a) acceleration of the three joints for the optimized trajectory and (b) acceleration in the workspace for the optimized trajectory

Grahic Jump Location
Fig. 13

Acceleration for the optimized trajectory (under constraints in Table 2)

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