Manipulator Grasp Control Using ToF Vision for Irregular Objects
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Solution Overview
Problem
Current robotic manipulators face challenges in accurately holding objects without a specified shape due to limitations in the arrangement of the manipulator and mechanical type, leading to high costs when using sensors on each finger for control.
Innovation Solution
A control method and system that utilizes a Time of Flight camera to detect and recognize targets, setting appropriate holding motions and forces based on material properties and shapes, allowing for stable and rapid holding control by adjusting the robot's motion and force in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are installed on each finger of the manipulator to enable accurate holding control, then the holding precision is improved, but the system cost increases significantly
Solution Approach 1:
The patent introduces a camera as an intermediary device to capture images of the target object, which then serves as input for deep learning-based recognition. This intermediary approach allows the system to obtain detailed object information (shape, size, material properties) without requiring expensive sensors on each manipulator finger, thereby resolving the contradiction between holding precision and system cost
Solution Approach 2:
The patent replaces the mechanical sensor-based detection system with an optical-based vision system using cameras and deep learning algorithms. This substitution eliminates the need for physical contact sensors on the manipulator, reducing hardware costs while maintaining or improving measurement precision through advanced image processing and material property recognition
2Device complexity
If the manipulator arrangement and mechanical type are simplified, then the device complexity is reduced, but the ability to accurately hold objects without specified shape deteriorates
Solution Approach 1:
The patent performs preliminary action by capturing images of the target object before the manipulator makes contact. The deep learning system processes these images to recognize object shape, size, and material properties in advance, allowing the manipulator to adjust its holding strategy beforehand. This preliminary recognition enables simple manipulator designs to achieve accurate holding of objects with unspecified shapes
Solution Approach 2:
The patent changes the parameter space from physical contact-based detection to optical parameter-based recognition. By analyzing image parameters (pixel intensity, edge detection, material reflectivity) through deep learning, the system derives object properties without requiring complex manipulator arrangements, thereby maintaining adaptability while reducing device complexity
3Stability of the object's composition
If the robot adjusts motion and holding force in real-time based on detected target properties, then the holding stability is improved, but the control complexity increases
Solution Approach 1:
The patent implements feedback by using the camera to continuously monitor the target object and feed this visual information back to the control system. The deep learning model processes this feedback to update recognition of object properties, which then adjusts the manipulator's holding force and motion in real-time. This closed-loop feedback mechanism improves holding stability while keeping control complexity manageable through efficient image processing
Solution Approach 2:
The system performs preliminary recognition of object properties (material, shape, size) before actual holding begins. This preliminary action allows the control system to pre-calculate appropriate holding forces and motion parameters, reducing the complexity of real-time control adjustments while maintaining high holding stability during manipulation
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate estimation of a target's mass and material, improving stability in holding control by setting initial holding postures and forces, facilitating rapid and precise object manipulation.
Implementation Method 1
The camera may be a Time of Flight (ToF) camera configured to capture an image of a target by transmitting and receiving pulses
Data Source
AI summary
A control method of a manipulator is provided. The method includes photographing a target using a camera and detected the target using the photographed data. A holding motion for the target is set based on the detected target and a robot is operated to hold the target based on the set holding motion.