Multi-Robot Consensus Control for Tight-Space Workpiece Rotation
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Solution Overview
Problem
When multiple robots are used to handle a large workpiece, they are limited by the volume of the workpiece and the external space, making it difficult to rotate the workpiece with a minimum rotation radius.
Innovation Solution
A data-driven bipartite consensus control method is implemented, which includes setting a multi-robot system with a static leader robot and follower robots, constructing a rotation dynamics model, and using a dynamic linearization technique to design a data-driven bipartite consensus controller that estimates unknown parameters and calculates a front wheel steering angle control signal for precise rotation control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple robots are used to handle a large workpiece, then the handling capability and coordination ability are improved, but the rotation radius requirement increases due to workpiece volume and external space limitations
Solution Approach 1:
The system divides the rotation control task into multiple coordinated robots, each responsible for specific aspects of workpiece manipulation. By segmenting the control functions across multiple robots with different roles (leader and followers), the system achieves complex rotation tasks within limited space
Solution Approach 2:
The patent introduces a hierarchical control structure with leader-follower relationships, adding a dimensional layer to the coordination problem. This allows the system to manage rotation complexity through temporal and informational dimensions rather than just spatial arrangements
2Device complexity
If a data-driven control method is used without accurate dynamic model, then the controller design complexity is reduced, but the precision of rotation control may be affected
Solution Approach 1:
The control method incorporates feedback mechanisms that use real-time data from robot positions and orientations to continuously adjust control inputs. This feedback loop compensates for the lack of accurate dynamic models while maintaining control precision through iterative correction
Solution Approach 2:
The system uses its own operational data to improve control performance without requiring external model information. By learning from its own input-output data, the controller adapts to the actual system behavior and achieves precise control through self-information
Data Source
AI summary
The provided is a data-driven bipartite consensus control method for multi-robot collaborative rotation of a large workpiece. The method includes: setting a multi-robot system; constructing and discretizing a dynamics model of a follower robot, and constructing a lateral error based on position information between the follower and leader robots; constructing an unknown nonlinear function with the lateral error and a control input as variables, and constructing a lateral error data model of the follower robot through a dynamic linearization technique; designing, based on a topological relationship of the multi-robot system and the lateral error, a bipartite consensus error; substituting the data model into a designed objective function to solve a data-driven bipartite consensus controller; designing a parameter estimation algorithm to estimate an unknown parameter in the controller; allowing an estimated value to participate in a controller update; and calculating a front wheel steering angle control signal.