Apply RRT-based path planning to robotic manipulation of biological cells with optical tweezer
ABSTRACT 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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ABSTRACT: High-precision motion of parallel manipulators depends not only on the position accuracy of each actuator, but also on the position synchronization of all actuators. This paper presents a simple synchronized control algorithm for the setpoint position control of parallel manipulators, by incorporating cross-coupling technology into a common proportional-derivative (PD) control architecture. An integrated controller is developed, consisting of a PD control and a saturated proportional-integral (S-PI) control with feedback of the differential position errors amongst actuators (defined as the synchronization errors). The controller can stabilize the motion of each actuator, and meanwhile synchronize all actuators' motions so that both position and synchronization errors converge to zero. The control algorithm does not use the modeling parameters in the controller formulation, and thus permits easy implementation in practice. It is proved that the proposed method can guarantee global asymptotical stability of the system. Experiments conducted on a planar three-degree-of-freedom parallel manipulator demonstrate the effectiveness of the proposed approach.IEEE Transactions on Robotics 03/2006; · 2.57 Impact Factor
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ABSTRACT: Automated particle transport using optical tweezers requires the use of motion planning to move the particle while avoiding collisions with randomly moving obstacles. This paper describes a stochastic dynamic programming based motion planning framework developed by modifying the discrete version of an infinite-horizon partially observable Markov decision process algorithm. Sample trajectories generated by this algorithm are presented to highlight effectiveness in crowded scenes and flexibility. The algorithm is tested using silica beads in a holographic tweezer set-up and data obtained from the physical experiments are reported to validate various aspects of the planning simulation framework. This framework is then used to evaluate the performance of the algorithm under a variety of operating conditions.IEEE Transactions on Automation Science and Engineering 05/2010; · 1.67 Impact Factor
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ABSTRACT: Automated optical trapping of non-spherical objects offers great flexibility as a non-contact micromanipulation tool in various research fields. Computer vision control enables fruitful applications of automated manipulation in biology and material science. Here we demonstrate fully-automated, simultaneous, independent trapping and manipulation of multiple non-spherical objects using multiple-force optical clamps. Customized real-time feature recognition and trapping beam control algorithms are also presented.Optics Express 10/2008; 16(19):15115-22. · 3.55 Impact Factor