Conference Paper

Apply RRT-based path planning to robotic manipulation of biological cells with optical tweezer

DOI: 10.1109/ICMA.2011.5985660 Conference: Mechatronics and Automation (ICMA), 2011 International Conference on
Source: IEEE Xplore


Cell transportation represents one of essential operations in many cellular engineering applications such as cell sorting and cell fusion, and has great potential in biomedical engineering and drug industry. This paper presents a path planning approach to cell transportation, where the cell is trapped by optical tweezer and controlled robotically to move towards the target position. To determine the collision-free path for the cell transportation, we develop a fast path planner based on RRT algorithm (Rapidly-exploring random trees). Simulation and experiment that are performed in transporting yeast cells demonstrate the effectiveness of the proposed approach.

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    • "[26] and [27] developed a control architecture for automated transport of single and multiple cells respectively using direct optical trapping without collision avoidance. [28] designed a local controller using a potential field method to position micro particles into arrays, while [29], [30] applied the sampling-based RRT algorithm to plan collision-free paths for directly trapped cells. However, none of these approaches explicitly model the inherent system and sensor measurement stochasticites in the planning framework . "
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    ABSTRACT: Automated transport of multiple particles using optical tweezers requires real-time path planning to move them in coordination by avoiding collisions among themselves and with randomly moving obstacles. This paper develops a decoupled and prioritized path planning approach by sequentially applying a partially observable Markov decision process algorithm on every particle that needs to be transported. We use an iterative version of a maximum bipartite graph matching algorithm to assign given goal locations to such particles. We then employ a three-step method consisting of clustering, classification, and branch and bound optimization to determine the final collision-free paths. We demonstrate the effectiveness of the developed approach via experiments using silica beads in a holographic tweezers setup. We also discuss the applicability of our approach and challenges in manipulating biological cells indirectly by using the transported particles as grippers.
    Full-text · Article · Oct 2012 · IEEE Transactions on Automation Science and Engineering
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    ABSTRACT: Manipulation of biological cells has recently drawn tremendous attention for its wide applications in biomedical fields such as cell-cell interaction, drug discovery, and tissue engineering. This paper presents a rapidly-exploring random trees (RRT) based path planner for transportation of biological cells in robotic transportation with optical tweezers in three dimensions (3D). By integrating the RRT algorithm into the optical tweezers manipulation system, we can successfully transport biological cells with high precision while avoiding obstacles during cell movement. Simulations and experiment are performed in transporting yeast cells to demonstrate the effectiveness of the proposed approach.
    No preview · Conference Paper · Aug 2011
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    ABSTRACT: This paper presents a path planning approach to the transportation of biological cells with combined robotics and optical tweezers technologies. A rapid path planner based on RRT (Rapidly-exploring random trees) algorithm is applied to find a collision-free path for automatic cell transportation. The optical tweezers are employed to trap and move the cell along the generated path toward a pre-specified goal position. Extending our early reported work on static path planning, a new dynamic path planner that considers the environmental change due to the Brownian movement of the cells is developed. This dynamic path planner can successfully enable the trapped cell to avoid collisions with other cells during transportation in a dynamic environment. Experiments on transporting yeast cells are performed to demonstrate the effectiveness of the proposed approach.
    No preview · Conference Paper · Oct 2012
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