Robot Picking Order Optimization for Conveyor Workpiece Windows
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Solution Overview
Problem
Existing robot systems lack an efficient method to pick multiple workpieces from a conveying surface, as they often rely on inflow order, which may not optimize the picking sequence based on the positions and movements of the workpieces, leading to suboptimal picking efficiency.
Innovation Solution
A robot system that sets a window of n workpieces, determines a picking order using an optimization algorithm like dynamic programming based on the positions of the workpieces, and selects m workpieces from this window for efficient picking, where n > m, allowing for repeated iterations to continuously pick workpieces efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workpieces are picked in inflow order, then the picking process is simple to implement, but the picking efficiency is suboptimal
Solution Approach 1:
The system performs preliminary actions by setting a window of n workpieces in advance and determining the optimal picking order for all workpieces before actual picking begins. The optimization algorithm calculates the best sequence considering positions and movements, allowing the robot to execute efficiently without real-time decision delays
Solution Approach 2:
The picking sequence is made dynamic through repeated iterations where the window of n workpieces is continuously updated as workpieces are picked and new workpieces enter the conveying surface. The optimization algorithm recalculates the picking order based on current positions and movements, adapting to changing conditions rather than following a fixed inflow order
2Adaptability or versatility
If the robot picks workpieces based on current positions only, then the picking process is simple, but it cannot adapt to changing workpiece movements
Solution Approach 1:
The system implements feedback by continuously monitoring the positions and movements of workpieces on the conveying surface during the picking process. The optimization algorithm uses this feedback information to adjust and recalculate the picking order in repeated iterations, ensuring the robot adapts to changing workpiece positions and maintains optimal picking efficiency
Solution Approach 2:
Before each picking operation, the system performs preliminary optimization by determining the picking order for the window of n workpieces based on their current positions and expected movements. This advance calculation allows the robot to anticipate and adapt to workpiece movements rather than reacting to them in real-time
Data Source
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AI summary
A robot system includes: a window setting unit configured to set, based on a plurality of workpieces that move along a conveying surface, a window corresponding to n workpieces of the plurality of workpieces, according to an inflow order that is an order in which the plurality of workpieces have moved along the conveying surface, where n > 1; an order determination unit configured to determine a picking order for the window by an optimization algorithm, based on a position of each of the n workpieces on the conveying surface, the picking order being an order of picking one or more of the workpieces from the conveying surface and being different from the inflow order; a target selection unit configured to select m workpieces from the window based on the picking order, where n > m; and a robot configured to pick the selected m workpieces from the conveying surface in the picking order.