Autonomous Task Sequencing for Movable Objects Under Energy Constraints
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Solution Overview
Problem
Current moveable objects, such as unmanned aerial vehicles, require human participation for decision-making and control, leading to increased time and labor costs and potential misoperation during task execution.
Innovation Solution
A computer-implemented task execution method for moveable objects that autonomously completes multiple tasks by determining a task mode, adjusting energy usage, and sending warnings for insufficient energy, allowing for optimal path planning and user-specified sequence execution, thereby reducing human intervention and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human participation is used for decision-making and control during task execution, then operational flexibility and adaptability are improved, but time costs and labor costs increase
Solution Approach 1:
The system performs preliminary action by pre-planning multiple task execution sequences (optimal path mode and user-specified sequence mode) before actual task execution. The moveable object autonomously determines the execution sequence based on energy status, task requirements, and environmental factors in advance, eliminating the need for real-time human decision-making and reducing time costs while maintaining operational flexibility.
Solution Approach 2:
The system applies self-service by enabling the moveable object to autonomously make decisions about task execution sequences without continuous human intervention. The onboard computer automatically monitors energy status, evaluates task priorities, and adjusts execution plans in real-time, allowing the system to serve itself and reducing both time costs and labor costs while maintaining adaptability.
2Adaptability or versatility
If human participation is used for decision-making and control during task execution, then operational flexibility and adaptability are improved, but labor costs increase
Solution Approach 1:
The system applies self-service by enabling the moveable object to autonomously make decisions about task execution sequences without continuous human intervention. The onboard computer automatically monitors energy status, evaluates task priorities, and adjusts execution plans in real-time, allowing the system to serve itself and reducing both time costs and labor costs while maintaining adaptability.
Solution Approach 2:
The system implements feedback by continuously monitoring the moveable object's energy status, task completion progress, and environmental conditions. This real-time feedback enables the onboard computer to dynamically adjust task execution sequences, ensuring optimal performance and adaptability while eliminating the need for human labor in decision-making processes.
3Productivity
If multiple tasks are executed during a single trip, then productivity is improved, but control complexity increases
Solution Approach 1:
The system applies segmentation by dividing multiple tasks into distinct executable units with defined priorities and energy requirements. Each task is treated as an independent segment that can be executed in optimized sequences, allowing the onboard computer to manage complexity through structured task decomposition while maintaining high productivity.
Solution Approach 2:
The system implements dynamics by enabling flexible adjustment of task execution sequences based on real-time energy status and task priorities. The control system dynamically reconfigures the execution plan during trips, allowing tasks to be executed in optimal sequences or skipped if energy is insufficient, thereby managing control complexity while maximizing productivity.
4Use of energy by moving object
If the moveable object executes tasks according to optimal path, then energy efficiency is improved, but task execution flexibility is reduced
Solution Approach 1:
The system implements dynamics by enabling flexible adjustment of task execution sequences based on real-time energy status and task priorities. The control system dynamically reconfigures the execution plan during trips, allowing tasks to be executed in optimal sequences or skipped if energy is insufficient, thereby managing control complexity while maximizing productivity.
Data Source
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AI summary
The present invention provides a task execution method and device. The task execution method includes: obtaining a task list, where the task list includes at least two to-be-executed tasks; determining a task mode, where the task mode includes a task set mode and a task flow mode; and executing the at least two to-be-executed tasks according to the determined task mode.