Robot Task Reordering for Dynamic Timing and Consumable Constraints
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Solution Overview
Problem
Current robots operate with rigid job descriptions and lack the ability to dynamically update and reorder tasks based on changing conditions, such as consumable levels or timing constraints, which can lead to inefficiencies and incomplete task completion.
Innovation Solution
A method for controlling self-propelled robots that involves feeding a service schedule with task parameters, allowing the robot to update information, derive a new order for uncompleted tasks, and perform them accordingly, taking into account factors like consumable levels, power availability, and timing constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a robot operates with a rigid job description and fixed task order, then the operation is simple and predictable, but the robot cannot adapt to changing conditions such as consumable levels or timing constraints
Solution Approach 1:
The patent implements dynamic task reordering where the robot continuously updates the execution sequence of tasks based on real-time conditions. The controller monitors consumable levels, timing constraints, and task dependencies, then dynamically recalculates the optimal task order. This transforms the rigid static task list into a flexible dynamic schedule that adapts to changing operational conditions without requiring complex manual intervention.
Solution Approach 2:
The system incorporates feedback mechanisms where the robot monitors its own state (consumable levels, current position, task completion status) and external conditions (timing constraints, environmental factors). This feedback is fed into the task management system which then adjusts the task execution order accordingly. The closed-loop control ensures the robot responds appropriately to changing conditions while maintaining overall operational efficiency.
2Reliability
If the robot performs tasks in a fixed order, then the control logic is simple, but timing requirements may not be met when conditions change
Solution Approach 1:
The patent applies preliminary action by pre-calculating multiple possible task sequences and their associated timing implications. When the robot begins execution, it has already prepared alternative task orders that account for potential conditions. If timing constraints are anticipated or detected, the system can switch to a pre-planned alternative sequence that guarantees timing compliance, rather than reacting too late to adjust the schedule.
Solution Approach 2:
The system dynamically changes task execution parameters including the sequence order, start times, and duration allocations based on real-time conditions. When timing requirements are at risk, the controller adjusts these parameters by reordering tasks to prioritize time-critical operations, rescheduling non-urgent tasks, or allocating different time windows to specific tasks, thereby maintaining timing compliance while preserving overall productivity.
3Reliability
If the robot does not update task information, then the operation is straightforward, but consumables may be depleted and tasks cannot be completed
Solution Approach 1:
The robot performs self-service by autonomously monitoring its own consumable levels (battery charge, cleaning solution, etc.) and task progression. The controller continuously updates task information based on self-reported status data, eliminating the need for external monitoring systems. When consumable levels drop below thresholds or tasks are completed, the system automatically updates the task list and adjusts subsequent task assignments, ensuring reliable task completion without adding complex external management infrastructure.
4Loss of time
If the robot reorders tasks dynamically, then adaptability to timing constraints is improved, but the control system becomes more complex
Solution Approach 1:
The patent segments the task management system into modular components: task definition module, condition monitoring module, optimization algorithm module, and execution control module. Each segment handles a specific aspect of dynamic reordering independently. This modular architecture reduces control complexity by allowing each component to focus on a single function while working together to achieve adaptive task scheduling that optimizes time efficiency.
Data Source
Figure 1~2
AI summary
A robot and a method of controlling the robot wherein the robot has a service schedule comprising a number of tasks. The robot receiving or deriving updated information and reordering a number of the tasks not yet performed in accordance with timing information in accordance with which the tasks must be completed.