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Genetic research relies on model organisms like C. elegans, microscopic nematodes valued for their genetic simplicity and short lifecycles. Researchers must physically transfer these worms between experimental conditions, a process traditionally performed manually with wire picks - creating significant bottlenecks in experimental throughput. Automation is critically needed for high-throughput studies. An integrated system combining a 6-axis robotic arm, motorized microscope, and AI-driven machine vision has been developed to address this challenge, known as WormPicker 2.0. This research presents the design and implementation of a comprehensive motion control architecture for WormPicker 2.0's Yaskawa GP4 robotic arm, enabling autonomous execution of complex protocols.

The developed software architecture follows a layered design pattern that transforms high-level experimental commands into precise robotic movements. At its core, the system employs ROS2 and MoveIt2 frameworks for motion planning and execution. It features a modular structure that separates concerns across five layers: interface, command processing, task generation, motion planning, and execution.

Key technical contributions include a command parser that processes multi-parameter instructions. The system also incorporates a task generation framework that dynamically creates motion sequences from workspace configurations. Additionally, it features a calibration system that maps between digital design models and physical workspace coordinates, along with multiple interfaces for remote network access and direct command-line control.

This motion control system enables WormPicker 2.0 to achieve a transfer rate of 13 animals per minute (4 times improvement over previous implementations) while maintaining precise navigation across 250 plates in a compact footprint. By automating worm manipulation, this work significantly enhances research capabilities for genetic screens, aging assays, drug response studies, and behavioral analysis. The modular software architecture provides a foundation for future enhancements, moving closer to a general-purpose tool for C. elegans laboratories that dramatically increases experimental throughput and reproducibility.