RobotSDF: Implicit Morphology Modeling for the Robotic Arm.

Area of Science:

• Robotics
• Computer Vision
• Computational Geometry

Background:

• Accurate robot arm morphology is crucial for motion planning and collision avoidance.
• Traditional methods struggle with the trade-off between computational cost and detail for dynamic shapes.
• Signed distance functions offer advantages but face challenges with infinite robot postures.

Purpose of the Study:

• Introduce RobotSDF, an implicit modeling approach for precise robot morphology expression across arbitrary postures.
• Address limitations of existing methods in representing dynamic robot shapes.
• Enhance efficiency and accuracy in robotic motion planning and collision avoidance.

Main Methods:

• RobotSDF models robot morphology as integrated implicit joint models driven by joint configurations.
• Dynamic shape changes are handled via coordinate transformations of query points within joint coordinate systems.
• An implicit, pose-aware approach is utilized, avoiding independent modeling of each posture.

Main Results:

• RobotSDF accurately depicts robot shapes across different postures to the millimeter level.
• Achieved 38.65% and 66.24% improvement over Neural-JSDF and configuration space distance field algorithms, respectively.
• Demonstrated efficient collision avoidance in simulation and real-world human-robot collaboration.

Conclusions:

• RobotSDF provides a precise and efficient method for implicit robot morphology modeling.
• The approach effectively handles dynamic shape changes and arbitrary robot postures.
• RobotSDF significantly advances capabilities in robotic motion planning and safe human-robot interaction.