Robot Task Reordering Under Time and Consumable Constraints
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Solution Overview
Problem
Existing robot operation methods struggle to efficiently manage task interruptions and reordering in dynamic environments, particularly when resources such as time, consumables, and space are limited.
Innovation Solution
A method for operating robots that involves scheduling a service schedule with multiple tasks, allowing operators to interrupt and reorder tasks based on parameters such as time, consumables, and space, and ensuring that the robot can adapt to changing conditions by determining the optimal order for remaining tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the robot performs multiple tasks according to a service schedule, then productivity is improved, but the complexity of managing task interruptions and reordering increases
Solution Approach 1:
The patent implements dynamic task reordering by allowing the robot to adjust the execution sequence of tasks based on real-time parameter changes. When an operator modifies parameters through the user interface, the system dynamically recalculates and reorders the service schedule, enabling flexible adaptation without fixed rigid scheduling.
Solution Approach 2:
The system incorporates feedback mechanisms where the robot continuously monitors task execution status and parameter changes. The user interface provides feedback to operators about current task states, and the system uses this feedback to automatically adjust task ordering, creating a closed-loop control system that manages complexity through intelligent response to system states.
2Adaptability or versatility
If the robot allows task interruptions for additional tasks, then adaptability is improved, but the time required to complete original tasks increases
Solution Approach 1:
The patent applies preliminary action by having the robot perform preparatory movements or partial task completions before interruptions occur. When additional tasks are introduced, the system calculates optimal interruption points and prepares for efficient resumption, minimizing time loss by having the robot positioned or partially completed tasks in advance.
Solution Approach 2:
The system manages time loss through parameter changes by adjusting task parameters such as priority levels, time allocations, and resource distributions when interruptions occur. The user interface allows operators to modify these parameters, and the robot adapts its execution accordingly, optimizing the balance between adaptability and time efficiency.
3Quantity of substance
If the robot optimizes task order based on parameters, then resource utilization is improved, but the computational complexity increases
Solution Approach 1:
The patent implements partial optimization by focusing computational efforts on critical parameters that have the greatest impact on resource utilization. Rather than optimizing all possible task parameters simultaneously, the system identifies and optimizes key parameters such as consumable consumption rates and time-critical tasks, reducing computational complexity while maintaining resource efficiency.
Solution Approach 2:
The robot performs self-service optimization by automatically adjusting task orders based on monitored parameter changes without requiring external intervention for every adjustment. The system uses pre-programmed optimization algorithms that autonomously respond to parameter changes, reducing the need for complex real-time computational analysis while maintaining resource efficiency.
Data Source
AI summary
A method of operating a robot performing a task exceeding a parameter. The robot performs the task and re-prioritizes the remaining tasks to have as many performed within the parameter as possible.