Robot Hand Grasp State Estimation for Slip-Stable Control
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Solution Overview
Problem
Existing robot control systems face instability in grasping objects due to changes in the coefficient of friction between the hand and the workpiece over time, and due to rotational moments acting on the object during grasping.
Innovation Solution
A control apparatus and method that utilize a hand controller to obtain information on grasping force and moment from sensors, allowing the estimation of the grasping state. This enables the system to correct the grasping force and approach to stabilize the grasping operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot hand grasps the object with initial grasping force, then the object can be held initially, but the grasping becomes unstable over time due to changes in friction coefficient and rotational moments
Solution Approach 1:
The hand controller continuously acquires sensor information about grasping force and moments acting on the object, estimates the current grasping state, and adjusts the grasping force in real-time based on this feedback. This closed-loop control ensures stable grasping over time by compensating for changes in friction coefficient and rotational moments.
Solution Approach 2:
The system dynamically changes the grasping force parameter based on estimated grasping states. By adjusting the magnitude of grasping force according to real-time conditions (friction changes, object orientation, applied moments), the system maintains reliable grasping throughout the operation duration.
2Reliability
If the robot uses simple grasping control without continuous adjustment, then the control system remains simple, but slippage occurs between the hand and workpiece during assembly or disassembly
Solution Approach 1:
The hand controller uses feedback from sensors to detect changes in grasping conditions and adjusts grasping force accordingly. This prevents slippage during assembly or disassembly operations with reaction forces, while keeping the control logic based on established estimation methods.
Solution Approach 2:
The system uses the robot's own sensors and control mechanisms to automatically adjust and maintain proper grasping force without external intervention. The hand controller self-regulates the grasping state based on real-time sensor data, preventing slippage autonomously.
3Measurement precision
If the robot does not estimate grasping state, then the control process remains simple, but accurate adjustment of grasping force cannot be achieved
Solution Approach 1:
The hand controller estimates the grasping state by processing sensor information about grasping force and moments. This estimation provides accurate feedback for adjusting grasping force, enabling precise control while using standard sensor data and calculation methods.
Solution Approach 2:
The system replaces direct mechanical measurement of grasping state with computational estimation based on sensor data. By calculating the grasping state from force and moment information rather than using complex mechanical sensors, the system achieves high measurement precision with moderate control process complexity.
Data Source
AI summary
A control apparatus includes a hand controller that controls a hand capable of grasping an object to be grasped and including a plurality of sensors. The hand controller is configured to be able to obtain information regarding grasping force acting on the object to be grasped from the hand or information regarding a moment acting on the object to be grasped in the hand, the information regarding the grasping force and the information regarding the moment being based on outputs of the plurality of sensors. The hand controller estimates, on a basis of the information regarding the grasping force or the information regarding the moment, a grasping state in which the hand is grasping the object to be grasped.