Autonomous Machine Dispatch Using Task Area Evaluation Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current management systems for mobile robotic devices lack effective methods to coordinate and manage multiple autonomous machines for executing both predictable and unpredictable physical tasks in diverse environments, especially in managing tasks across multiple locations and environments efficiently.
Innovation Solution
A networked workforce computing system that includes user devices, autonomous machines, a task management server, and a processor for computer communication, which receives service requests, evaluates task areas, determines tasks, selects appropriate autonomous machines, and controls their movement and task execution based on evaluation data and location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple autonomous machines are deployed to execute tasks in diverse environments, then task execution capability and versatility are improved, but system complexity and coordination difficulty increase
Solution Approach 1:
The patent introduces a centralized server as an intermediary that receives service requests, evaluates task areas using evaluation data, determines appropriate tasks, and assigns them to suitable autonomous machines. This mediator coordinates the complex interactions between multiple machines and diverse environments, managing the versatility while containing system complexity through centralized control logic.
Solution Approach 2:
The system segments the workforce management into distinct functional modules: service request reception, task area evaluation, task determination, and machine selection. Each module handles a specific aspect of the coordination process, allowing the system to manage multiple autonomous machines across diverse environments by breaking down the complex coordination task into manageable segments.
2Productivity
If centralized workforce management is implemented to coordinate multiple autonomous machines, then task allocation efficiency is improved, but communication overhead and system latency increase
Solution Approach 1:
The system performs preliminary evaluation of task areas by collecting and analyzing evaluation data before assigning tasks to autonomous machines. By pre-evaluating task areas and preparing task assignments in advance, the centralized server reduces real-time decision-making latency, improving task allocation efficiency while minimizing communication overhead during actual task execution.
3Measurement precision
If evaluation data is collected about task areas to improve task determination accuracy, then task execution precision is improved, but data acquisition time and system response time increase
Solution Approach 1:
The system collects and processes evaluation data about task areas in advance, before task assignment is required. This preliminary data collection enables accurate task determination when needed, improving measurement precision while reducing the time penalty during actual task allocation by having evaluation information ready beforehand.
Data Source
AI summary
A method for workforce management includes receiving a service request associated with a task area from a user device, and controlling movement of an unmanned aerial machine from a home base to the task area. The unmanned aerial machine acquires evaluation data about the task area. The method also includes determining a task to be performed based on the service request, the task area, and the evaluation data. Further, the method includes selecting one or more autonomous machines to perform the task based on at least the task and a location of the task area, and controlling the selected one or more autonomous machines to perform the task