Server-Based Robot Task Allocation via Segmentation
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Solution Overview
Problem
Current robot systems are limited in their ability to provide various services efficiently and cost-effectively, as they often require significant administrator intervention and are not optimized for different service types and locations, lacking a coordinated approach for multiple robots to work together.
Innovation Solution
A robot system and method that enables multiple robots to cooperate by using a server to select and allocate the most suitable robots for tasks based on their current tasks, location, and availability, allowing for efficient service provision with minimal administrator intervention, including the use of different robot types for specific tasks such as guidance and load carrying.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single robot performs all tasks, then the system structure is simple, but the service efficiency and versatility deteriorate
Solution Approach 1:
The patent divides the robot system into multiple independent robots, each specialized for specific tasks (guide robot, porter robot, etc.). This segmentation allows the system to provide diverse services through coordinated operation of specialized units, resolving the contradiction between service versatility and system complexity by distributing functionality across multiple simple, modular components rather than requiring one complex universal robot
Solution Approach 2:
The server acts as a universal coordinating platform that manages multiple different types of robots, enabling the overall system to achieve versatility through centralized task allocation. The server can assign various tasks to appropriate robot types based on service requirements, achieving multi-functionality at the system level while keeping individual robot units relatively simple
2Productivity
If administrator manually assigns tasks to robots, then task allocation is precise, but the operation complexity and time consumption increase
Solution Approach 1:
The system implements automated task allocation where the server independently assigns tasks to robots based on current service requests, robot availability, and task requirements. This self-service mechanism eliminates the need for manual administrator intervention in task assignment, significantly improving task allocation efficiency while reducing operational complexity and time consumption
Solution Approach 2:
The server continuously monitors robot status, location, and task completion information, using this feedback to dynamically allocate new tasks. This real-time feedback mechanism enables efficient automated task distribution, allowing the system to adapt to changing conditions without administrator intervention and maintain optimal productivity
3Reliability
If multiple robots are deployed for various services, then service quality improves, but the cost and system complexity increase
Solution Approach 1:
The system segments robot functions into specialized units (guide robots for navigation, porter robots for carrying loads, etc.), allowing high service quality through task-specific optimization while keeping individual robot designs relatively simple and cost-effective. Each segmented robot unit focuses on specific functions, improving reliability without requiring every robot to be a complex multi-functional platform
Solution Approach 2:
Multiple specialized robots are merged into a coordinated system managed by a central server, achieving high service quality through collaborative operation. The server integrates the capabilities of different robot types to provide comprehensive services, maintaining reliability while avoiding the need for each individual robot to be overly complex
4Productivity
If robots are allocated without optimization, then system operation is simple, but resource utilization and service efficiency deteriorate
Solution Approach 1:
The server implements optimized task allocation by continuously receiving feedback on robot status, location, battery level, and task progress. This feedback enables dynamic decision-making for task assignment, improving resource utilization by assigning tasks to the most suitable available robots rather than using simple first-come-first-served allocation, while maintaining manageable system complexity through automated algorithms
Data Source
AI summary
Disclosed is a method of controlling a robot system, including receiving user input including a request for a predetermined service, by a first robot, transmitting information based on the user input to a server, by the first robot, identifying a support robot for supporting a task corresponding to the service request, by the server, making a request to the second robot identified to be the support robot for the task, by the server, and performing the task, by the second robot, wherein the first robot is different from the second robot.