Predictive End-Effector Positioning for Continuum Robot Control

The patent applies preliminary action by training the neural network model beforehand using extensive simulation data that encompasses various environmental conditions, manufacturing tolerances, and operational scenarios. This pre-training enables the model to inherently account for these variations without requiring recalibration, thus improving reliability while maintaining positioning accuracy through feedforward prediction capabilities.

The patent replaces the traditional static mechanical model with nonlinear differential equations with a data-driven neural network model. This substitution allows the system to learn complex deformation patterns from data rather than relying on explicit mathematical formulations, making the model more robust to environmental changes and manufacturing inaccuracies while maintaining high positioning accuracy.

The patent replaces complex analytical kinematics calculations with a neural network-based predictive model. The network learns the mapping between joint configurations and end-effector positions from simulation data, eliminating the need for real-time solution of complex kinematic equations. This substitution maintains high maneuverability while significantly reducing computational complexity and control difficulty.

The patent performs preliminary computation during the training phase by pre-computing the relationship between joint space and task space across the entire workspace. This offline training enables the controller to simply query pre-learned mappings during operation, avoiding real-time complexity while maintaining full maneuverability of the high-degree-of-freedom robot.

The patent performs comprehensive calibration and adaptation during the offline training phase, where the neural network learns from simulation data that incorporates manufacturing tolerances and expected wear patterns. This preliminary adaptation embeds compensation strategies into the model itself, reducing the need for frequent recalibration while maintaining high positioning precision even as components wear.

The patent employs parameter changes by training the neural network to be robust to variations in mechanical parameters such as link lengths, joint clearances, and material properties. The model learns to compensate for these parameter variations through exposure to diverse training scenarios, thereby maintaining positioning precision without requiring precise calibration of each individual parameter.