Non-contact Dexterous Micromanipulation with Multiple Optoelectronic Robots

Abstract

Micromanipulation systems leverage automation and robotic technologies to improve the precision, repeatability, and efficiency of various tasks at the microscale. However, current approaches are typically limited to specific objects or tasks, which necessitates the use of custom tools and specialized grasping methods. This paper proposes a novel non-contact micromanipulation method based on optoelectronic technologies. The proposed method utilizes repulsive dielectrophoretic forces generated in the optoelectronic field to drive a microrobot, enabling the microrobot to push the target object in a cluttered environment without physical contact. The non-contact feature can minimize the risks of potential damage, contamination, or adhesion while largely improving the flexibility of manipulation. The feature enables the use of a general tool for indirect object manipulation, eliminating the need for specialized tools. A series of simulation studies and real-world experiments -- including non-contact trajectory tracking, obstacle avoidance, and reciprocal avoidance between multiple microrobots -- are conducted to validate the performance of the proposed method. The proposed formulation provides a general and dexterous solution for a range of objects and tasks at the micro scale.

Figures (10)

• Figure 1: Overview of the proposed framework for non-contact micromanipulation using multiple optoelectronic robots. Projection light-driven general micro-robots use dielectrophoretic repulsion to manipulate targets indirectly. The repulsion can be modeled as a nonlinear model and simplified into a virtual link model. Both global and local planners utilize the virtual link model while considering obstacle avoidance among multiple robot systems and static obstacles. The controller ensures robust tracking of the reference trajectory. (Dashed line: Model loop. Solid line: Closed loop.)
• Figure 2: Illustration of an optoelectronic-driven robot performing non-contact manipulation on the target object. The complete nonlinear model, the simplified virtual link model, and the local linear model are proposed and used for control.
• Figure 3: Control diagram of the proposed method. The FF--FB controller generates the nominal trajectory, and the linear MPC controller performs fine-tuning.
• Figure 4: Illustration of the collision-free constraints between different agents. In each time period, the trajectories of the two robot--object pairs are represented as polyhedral and separated by a vector.
• Figure 5: Variation in tracking error over time for different controllers in simulations. The mean values (solid lines) and standard deviation ranges (shaded areas) are shown.