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Research Papers

Trajectory Planning for Micromanipulation With a Nonredundant Digital Microrobot: Shortest Path Algorithm Optimization With a Hypercube Graph Representation

[+] Author and Article Information
Vincent Chalvet

AS2M Department,
FEMTO-ST Institute,
Universite Bourgogne Franche-Comte,
Universite de Franche-Comte/CNRS/ENSMM,
24 rue Savary,
Besançon F-25000, France
e-mail: vchalvet@gmail.com

Yassine Haddab

AS2M Department,
FEMTO-ST Institute,
Universite Bourgogne Franche-Comte,
Universite de Franche-Comte/CNRS/ENSMM,
24 rue Savary,
Besançon F-25000, France
e-mail: yassine.haddab@femto-st.fr

Philippe Lutz

AS2M Department,
FEMTO-ST Institute,
Université Bourgogne Franche-Comté,
Université de Franche-Comté/CNRS/ENSMM,
24 rue Savary,
Besançon F-25000, France
e-mail: philippe.lutz@femto-st.fr

Manuscript received March 9, 2015; final manuscript received September 22, 2015; published online November 24, 2015. Assoc. Editor: Jian S. Dai.

J. Mechanisms Robotics 8(2), 021013 (Nov 24, 2015) (9 pages) Paper No: JMR-15-1053; doi: 10.1115/1.4031807 History: Received March 09, 2015; Revised September 22, 2015; Accepted September 29, 2015

Microrobotics is an ongoing study all over the world for which design is often inspired from macroscale robots. We have proposed the design of a new kind of microfabricated microrobot based on the use of binary actuators in order to generate a highly accurate and repeatable tool for positioning tasks at microscale without any sensor (with open-loop control). Our previous work consisted in the design, modeling, fabrication, and characterization of the first planar digital microrobot. In this paper, we focus on the motion planning of this robot for micromanipulation tasks. The complex motion pattern of this robot requires the use of algorithms. Graph theory is well suited for the discrete workspace generated by this robot. The comparison between several well-known trajectory-planning algorithms is done. A new graphical representation, named the hypercubic graph, is used for improving the computation speed of the algorithm. This is particularly useful for large workspace robots.

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References

Figures

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Fig. 1

Kinematic structure of the DiMiBot, with 2N bistable modules

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Fig. 2

Digital microrobot containing four bistable modules (N = 2) and a zoomed view of the end-effector while manipulating a 150 μm diameter glass ball

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Fig. 3

Workspace of two different digital microrobots: one containing six bistable modules (N = 3) and one containing 8 (N = 4)

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Fig. 4

Workspace numbering for N = 2 and the corresponding state of the bistable modules [bl1bl0br1br0]

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Fig. 5

Illustration of the thin tip going from the robot's end-effector down to the working plane

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Fig. 6

The two shortest paths when going from points 6–10 (in the case N = 2)

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Fig. 7

Path for reaching two points inside a workspace containing 256 reachable positions (DiMiBot for N = 4)

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Fig. 8

Graph representation of the workspace of a four bistable modules DiMiBot (N = 2). Possible displacements from node 6 are highlighted.

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Fig. 9

Weighted adjacency matrix 2/2 corresponding to the graph of Fig. 8

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Fig. 10

Shortest path found with the Dijkstra's algorithm in the case of a DiMiBot with ten bistable modules (N = 5) and with the presence of 0–5 obstacles

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Fig. 11

Cubic graph of DiMiBots containing 2, 4, and 6 bistable modules, respectively, of hypercubes of dimension 1, 2, and 3. In the hypercube of dimension 2, the node 6 and its neighbors are highlighted.

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Fig. 12

Shortest path found by different algorithms

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Fig. 13

Comparison in computation time for the different algorithms in the cases for robots up to 20 bistable modules (N = 10).

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