Independent Vision Control for Robotic Landmark-Guided Manipulation
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Solution Overview
Problem
Existing automation systems face challenges in reliable landmark detection due to the difficulty in developing feature extraction schemes suitable for various objects, adjusting camera views in dynamic environments, and simultaneously controlling vision systems and robotic manipulators.
Innovation Solution
An automation system comprising a manipulator, a movable vision system with independently controllable cameras, and a learning and control module that adjusts the camera's field of view and controls the manipulator to maximize the distance between objects and targets within the camera's view, using a convolutional network for landmark detection and adversarial control to achieve precise positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed feature extraction scheme is used for landmark detection, then the detection algorithm can be simple, but it cannot adapt to various objects and machine components
Solution Approach 1:
The patent implements a dynamic feature extraction scheme where the vision system continuously adapts its detection parameters based on the specific object being observed. The system switches between different feature extraction algorithms and adjusts detection thresholds dynamically according to object type, material properties, and environmental conditions, enabling universal applicability while maintaining detection accuracy.
2Device complexity
If the camera field of view is fixed, then the vision system structure is simple, but it cannot maintain optimal detection in dynamic environments where objects move
Solution Approach 1:
The patent employs a movable camera system with adjustable field of view that dynamically tracks objects and adapts its viewing parameters. The camera can pan, tilt, and zoom to maintain optimal detection angles and distances as objects move, ensuring continuous reliable landmark detection while providing real-time visual feedback for robotic manipulation.
3Device complexity
If the vision system and robotic manipulator are controlled separately, then the control system is simple, but it is difficult to simultaneously and automatically control both systems for coordinated tasks
Solution Approach 1:
The patent integrates the vision system and robotic manipulator control into a unified automated control architecture. The system merges visual data processing with manipulator motion planning, enabling simultaneous coordinated control where landmark detection results directly drive manipulator actions. This integration allows fully automated tasks such as visual servoing, assembly operations, and adaptive manipulation without separate manual intervention.
4Device complexity
If the camera is mounted on the manipulator, then the system structure is compact, but the camera field of view becomes limited and blocked during manipulation operations
Solution Approach 1:
The patent segments the vision system into multiple independent camera units with different mounting positions and viewing angles. By distributing cameras across multiple locations on the manipulator structure, the system achieves compact integration while ensuring that at least one camera maintains an unobstructed view of the workspace and target objects during manipulation operations.
Data Source
AI summary
An automation system includes a manipulation system including a manipulator for moving an object to a target location, a vision system for detecting landmarks on the object and the target location, and a learning and control module. The vision system is movable. The learning and control module is configured to control a movement of the manipulator and change a field of view of the vision system independent of the movement of the manipulator.