Cooperative Robot Motion Weighting for Redundancy and Collision Control
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Solution Overview
Problem
Current robotic systems face inefficiencies in handling redundant degrees of freedom during complex tasks, leading to challenges in motion planning and increased processor usage, as existing methodologies often get stuck in local minima and are NP-hard, failing to account for practical user concerns in manufacturing environments.
Innovation Solution
A computer-implemented method for distributing cooperative motion between two manipulators in a manufacturing processing system, allowing users to adjust weighting factors to specify motion percentages, calculating and distributing translation and rotation motions based on these factors to optimize joint movements and reduce redundancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous optimization methods are used to resolve motion redundancies, then collision constraints can be included and minimum time solutions can be found, but the methodology gets stuck in local minima and requires intensive processor usage
Solution Approach 1:
The patent segments the redundant motion resolution into discrete, selectable distribution options rather than continuous optimization. Users can choose from predefined motion distribution percentages (e.g., 0%, 25%, 50%, 75%, 100%) for each manipulator, converting a continuous NP-hard optimization problem into discrete selectable options that are computationally efficient to process.
Solution Approach 2:
The system dynamically adjusts motion distribution between manipulators based on user-selected parameters and real-time system state. The weighting factors for motion distribution can be modified during operation to optimize performance, reduce collisions, or minimize processor usage depending on operational requirements.
2Adaptability or versatility
If more than one manipulator is used to perform complex tasks, then task capability is improved, but motion redundancy increases making motion planning challenging
Solution Approach 1:
The patent introduces user-configurable weighting parameters that define motion distribution percentages for each manipulator. By changing these parameters, the system can adapt motion planning to different task requirements without increasing computational complexity. The parameters transform a complex redundancy resolution problem into a straightforward parameter assignment task.
Solution Approach 2:
The motion distribution weighting factors act as intermediaries between the task requirements and the actual manipulator motions. Instead of directly solving the complex redundancy resolution, the system uses these weighting parameters to mediate and simplify the motion planning process, making it more manageable and less computationally intensive.
3Ease of operation
If user-adjustable weighting factors are implemented, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The system provides user-adjustable weighting factors for motion distribution, but these factors only control the percentage of motion for each manipulator without requiring users to configure complex optimization algorithms or constraint systems. This partial action approach gives users sufficient control for most applications without exposing them to the full complexity of motion redundancy resolution.
Data Source
AI summary
The present invention features a computer-implemented method for adjustably distributing cooperative motion between a first manipulator and a second manipulator in a manufacturing processing system. The method includes receiving, by a computing device, data for the first manipulator configured to hold a tool, data for the second manipulator configured to hold a workpiece, and process data defining a process to be performed by the tool on at least a portion of the workpiece. The data for at least one of the first or second manipulator comprises a weighting factor adjustable by a user to specify at least a percentage of motion for the corresponding manipulator. The method also includes generating a relative transformation function for defining the process path and distributing motions between the first and second manipulators to complete the process path based on the at least one weighting factor.