Robotic Part Reorientation Using Robust External-Contact Pivoting
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Solution Overview
Problem
Current robotic assembly systems rely on meticulously designed part orientation and feeding systems, which are domain-specific, costly, and fragile when dealing with uncertainty in part properties during manipulation tasks.
Innovation Solution
A bilevel trajectory optimization method is proposed for robust pivoting manipulation using external contacts, which maximizes frictional stability by redistributing contact forces to accommodate uncertainty in object parameters, ensuring stability and generalization across different objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If specialized part orientation and feeding systems are used, then manipulation reliability is improved, but device complexity and cost increase
Solution Approach 1:
The object itself provides the contact point for manipulation, eliminating the need for specialized feeding systems. The method uses the object's own geometry and friction properties to enable stable manipulation through pivoting motions, allowing the system to adapt to different objects without specialized components.
Solution Approach 2:
The method changes the manipulation approach from relying on specialized mechanisms to using friction-based pivoting with variable contact points. By adjusting the pivoting motion and contact force parameters, the system achieves reliable manipulation across different objects without complex feeding systems.
2Manufacturing precision
If domain-specific part feeders are designed, then manipulation precision is improved, but adaptability deteriorates
Solution Approach 1:
The pivoting manipulation method is universally applicable to various objects with different geometries and friction properties. Instead of designing domain-specific feeders, the method uses a generalizable approach that adapts to different objects by identifying suitable contact points and adjusting pivoting parameters, achieving both precision and versatility.
Solution Approach 2:
The method employs dynamic pivoting motions that can adapt to different object properties during manipulation. The contact point and motion trajectory are adjusted in real-time based on object characteristics, enabling precise manipulation across diverse object types without specialized feeders.
3Adaptability or versatility
If friction-based pivoting manipulation is used, then adaptability is improved, but control difficulty increases due to uncertainty in friction parameters
Solution Approach 1:
The method incorporates feedback mechanisms to monitor and adjust contact forces during pivoting manipulation. By using tactile sensors and force feedback, the system compensates for uncertainty in friction parameters in real-time, maintaining stable control while adapting to different objects with varying friction properties.
Solution Approach 2:
The optimization approach anticipates friction parameter uncertainties by designing trajectories and contact forces that maintain stability even under varying friction conditions. The method pre-compensates for potential friction variations by selecting robust pivoting paths and contact points that ensure stable manipulation across a range of friction values.
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
The method enables robust and reliable manipulation of objects with uncertain physical properties by maximizing the frictional stability margin, allowing for efficient reorientation of parts during assembly without the need for specialized jigs and fixtures.
Implementation Method 1
pivoting manipulation that requires sustained slipping contact with the external environment... exploiting friction
Data Source
Figure 1A~1B
Figure 2
Figure 3A
AI summary
A manipulation controller is provided for reorienting an object by a manipulator of a robotic system. The manipulation controller includes an interface controller configured to acquire measurement data from sensors arranged on the robotic system, at least one processor, and a memory configured to store a computer-implemented method. The instructions of the method include acquiring measurement data from vision sensors and force sensors arranged on the robotic system, determining an input-output relation for the object based on a nonlinear static model representing input-output relationships between contact forces and movements of the object on the workbench, representing interaction between the object and the manipulator using complementarity constraints to capture the contact state between the object and the manipulator, formulating a representation for frictional stability of the object based on the non-linear static model at the external contacts with the workbench; formulating a bilevel optimization problem so as to maximize the frictional stability over a position trajectory of the object being manipulated on the workbench, estimating uncertainty value in physical parameters to be compensated by performing the bilevel optimization problem, solving the bilevel optimization problem using the non-linear optimization solver and generating control data with respect to a sequence of the contact forces being applied to the object by using the manipulator.