Admittance-Based Motion Planning with Vision-Guided Initialization for Robotic Manipulators in Self-Driving Laboratories

TL;DR

This paper presents an admittance-based motion planning framework with vision-guided initialization for robotic manipulators in self-driving laboratories, enhancing safety, adaptability, and human-robot interaction capabilities.

Contribution

It introduces a novel integration of admittance control directly into trajectory execution combined with vision-based target localization for improved robotic manipulation in SDLs.

Findings

Successfully demonstrated vision-based initialization for target detection.

Validated admittance control for safe, compliant motion during interaction.

Framework enhances safety and adaptability in laboratory robotic tasks.

Abstract

Self driving laboratories (SDLs) are highly automated research environments that leverage advanced technologies to conduct experiments and analyze data with minimal human involvement. These environments often involve delicate laboratory equipment, unpredictable environmental interactions, and occasional human intervention, making compliant and force aware control essential for ensuring safety, adaptability, and reliability. This paper introduces a motion-planning framework centered on admittance control to enable adaptive and compliant robotic manipulation. Unlike conventional schemes, the proposed approach integrates an admittance controller directly into trajectory execution, allowing the manipulator to dynamically respond to external forces during interaction. This capability enables human operators to override or redirect the robot's motion in real time. A vision algorithm based on structured planar pose estimation is employed to detect and localize textured planar objects through feature extraction, homography estimation, and depth fusion, thereby providing an initial target configuration for motion planning. The vision based initialization establishes the reference trajectory, while the embedded admittance controller ensures that trajectory execution remains safe, adaptive, and responsive to external forces or human intervention. The proposed strategy is validated using textured image detection as a proof of concept. Future work will extend the framework to SDL environments involving transparent laboratory objects where compliant motion planning can further enhance autonomy, safety, and human-robot collaboration.