Robot Path Constraints for Stable Vacuum Grasp Motion
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Solution Overview
Problem
Current robotic systems face challenges in maintaining grasp stability during motion, leading to object drops, especially when using vacuum grippers, due to inadequate consideration of pose, velocity, and acceleration, and existing approaches are either time-consuming or ineffective for complex tasks.
Innovation Solution
A computing system generates path constraints, including grasp pose, velocity, and acceleration, based on object and robot configuration data to ensure stable object transportation by formulating and solving optimization problems, and simulating trajectories to determine optimal motion parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert knowledge is used to implement grasping for individual use cases, then grasp accuracy is improved, but implementation time and cost increase
Solution Approach 1:
The patent implements a self-service approach where the system automatically generates path constraints using object physical properties and robot configuration data without requiring expert knowledge input. The automated generation of constraints based on measurable parameters eliminates the need for time-consuming expert implementation while maintaining high grasp accuracy.
Solution Approach 2:
The patent replaces expert knowledge with objective parameters such as object physical properties (mass, dimensions, center of gravity) and robot configuration data. By formulating grasp constraints in terms of these measurable parameters, the system achieves high accuracy through automated computation rather than subjective expert judgment, significantly reducing implementation time.
2Reliability
If path constraints are generated considering pose, velocity, and acceleration, then object transportation safety is improved, but computational complexity increases
Solution Approach 1:
The patent segments the path constraint generation into distinct components: object physical properties, robot configuration data, and motion parameters (pose, velocity, acceleration). By breaking down the complex problem into manageable segments that can be processed independently and then integrated, the system reduces computational complexity while maintaining comprehensive safety considerations.
Data Source
AI summary
In some cases, grasp point algorithms can be implemented so as to compute grasp points on an object that enable a stable grasp. It is recognized herein, however, that in practice a robot in motion can drop the object or otherwise have grasp issues when the object is grasped at the computed stable grasp points. Path constraints that can differ based on a given object are generated while generating the trajectory for a robot, so as to ensure that a grasp remains stable throughout the motion of the robot.