Multi-Robot Assembly Planning With Partial-Order Task Allocation
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Solution Overview
Problem
Assembly lines with dedicated robots for non-mass-produced products face high investment costs and low throughput due to slower robot operations, and existing methods lack efficient planning for multiple robots to improve assembly efficiency.
Innovation Solution
A device that plans assembly operations using a partial-order graph, operation sequence templates, part information, and layout data to allocate robots efficiently, calculate movement times, and select optimal allocation plans for high throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a multifunctional robot cell is used to assemble non-mass-produced products, then investment cost is reduced, but throughput decreases due to slower robot operation speed
Solution Approach 1:
The patent employs multiple multifunctional robots that can perform various assembly tasks, allowing a single robot unit to handle multiple operations. This reduces investment cost compared to dedicated robot assemblies while maintaining flexibility for non-mass-produced products
Solution Approach 2:
The patent combines multiple multifunctional robots into a coordinated system where they work simultaneously on different assembly operations. This merging of multiple robot units increases overall throughput while maintaining the cost benefits of using multifunctional rather than dedicated robots
2Productivity
If multiple robots are used to improve throughput, then productivity increases, but operation planning complexity increases due to coordination requirements
Solution Approach 1:
The patent divides the assembly task into multiple independent operations represented as nodes in a graph, where each node corresponds to a specific assembly action. This segmentation allows complex assembly tasks to be distributed across multiple robots, increasing throughput while making planning more manageable through modular task allocation
Solution Approach 2:
The patent uses graph analysis to determine optimal task allocation and sequencing based on operation dependencies. The system calculates and adjusts robot assignments based on feedback from operation times and constraints, optimizing the coordination of multiple robots to handle complexity while maximizing productivity
Data Source
AI summary
An assembly planning device: holds a partial-order graph showing assembly operations of parts and an order constraint based on a partial-order relationship of the assembly operations, an operation sequence template showing an operation sequence and a required time of the operation sequence, and part information showing parts that can be assembled by robots; refers to the part information to generate allocation plans in which the robot are allocated to the assembly operations shown by the partial-order graph; for each of the allocation plans, refers to the operation sequence template to allocate an operation sequence for each assembly operation shown by the allocation plan; calculates movement times of the robots in the allocated operation sequence; calculates an operation time in the allocation plan based on the movement times and the required time shown by the operation sequence template; and selects an allocation plan based on the operation times.