Robot Grasping End Effector Positioning via CNN-Based Grasp Detection

The patent replaces traditional mechanical and geometric reasoning systems with a convolutional neural network that processes image data to directly output grasping parameters. The CNN learns optimal grasping strategies through training on labeled examples, substituting complex mechanical positioning calculations with data-driven pattern recognition that achieves superior accuracy.

The patent introduces an image processing intermediary layer between the robot's visual system and its grasping control system. The CNN processes depth and color image data to extract meaningful features and generate grasping parameters, serving as an intelligent mediator that translates raw visual information into actionable control commands for the end effector.

The patent performs preliminary action by pre-training the convolutional neural network on extensive labeled datasets containing multiple valid grasping examples for various objects. This pre-training enables the network to rapidly infer multiple plausible grasping options during actual operation without requiring extensive real-time computation, thus providing adaptability with minimal processing delay.

The patent implements dynamics by allowing the system to adaptively select from multiple grasping options based on the specific object characteristics and task requirements. The CNN dynamically generates different grasping parameters (position, orientation, quaternion) tailored to each situation, enabling flexible adaptation while maintaining efficient processing through the learned model.