Robot Remote Gripping Control With Reduced 6-DOF Burden
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Solution Overview
Problem
Existing robot remote operation systems face challenges in accurately controlling 6-degrees-of-freedom hand tip targets in three-dimensional space, especially for users without advanced operation skills, leading to reduced work efficiency and accuracy due to trade-offs between followability, flexibility, and stability in inverse kinematics calculations, and delays in motion control which affect user comfort and work speed.
Innovation Solution
A robot remote operation control device that acquires operator state information, estimates motion intentions, and determines gripping methods, allowing for accurate object manipulation by limiting the degree of freedom and generating appropriate control commands based on the estimated motion, thereby improving work efficiency and reducing operator burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inverse kinematics calculation is used to control robot arm joints, then the robot can achieve precise 6-degrees-of-freedom hand tip positioning, but the system complexity and computational burden increase significantly
Solution Approach 1:
The patent segments the control problem by separating hand tip position control from gripper orientation control. The hand tip position is controlled through inverse kinematics for the robot arm, while the gripper orientation is independently controlled based on the object's surface normal. This segmentation reduces the coupling complexity of the full 6-DOF control problem.
Solution Approach 2:
The patent performs preliminary calculation of the object's surface normal vector before gripper orientation control. By pre-calculating the surface normal from point cloud data or object models, the system prepares orientation information in advance, reducing real-time computational burden during control execution.
2Adaptability or versatility
If full 6-degrees-of-freedom control is implemented, then the robot can perform complex manipulation tasks, but the control difficulty and operator skill requirement increase
Solution Approach 1:
The robot performs self-adjustment of gripper orientation based on automatically acquired object geometry information. The system uses sensors to detect object surface normals and autonomously calculates the appropriate gripper orientation, eliminating the need for operators to manually compute complex 6-DOF poses and reducing the skill barrier for operation.
Solution Approach 2:
The patent replaces manual operator judgment and calculation with automated sensor-based object recognition and computational geometry processing. Instead of relying on operator expertise to determine gripper orientation, the system uses point cloud processing and surface normal calculation to automatically determine optimal gripping configurations.
3Manufacturing precision
If precise positioning control is required for object manipulation, then task accuracy improves, but work speed decreases due to control delays
Solution Approach 1:
The system pre-calculates gripper orientation based on object surface normals before execution. By preparing orientation information in advance through automated geometric calculations, the system reduces real-time control computation time, enabling faster response while maintaining positioning accuracy.
Solution Approach 2:
The patent replaces slow manual operator positioning with automated sensor-based object detection and computational geometry processing. This substitution enables rapid determination of accurate gripper positions and orientations, improving both speed and precision of object manipulation.
Data Source
AI summary
A robot remote operation control device includes, in robot remote operation control for an operator to remotely operate a robot capable of gripping an object, an information acquisition unit that acquires operator state information on a state of the operator who operates the robot, an intention estimation unit that estimates a motion intention of the operator who causes the robot to perform a motion, on the basis of the operator state information, and a gripping method determination unit that determines a gripping method for the object on the basis of the estimated motion intention of the operator.