Collaborative Robot Task Allocation Without a Management Server
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Solution Overview
Problem
Existing robots in indoor spaces cannot provide seamless services when a management server fails, as task assignments are dependent on the server's functionality, leading to service disruptions.
Innovation Solution
A robot equipped with sensors, a driver, and a processor that acquires context data, communicates with other robots, and uses a task allocation algorithm to identify and assign tasks independently, allowing for seamless service provision without relying on a management server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robots depend on a management server for task assignment, then centralized control is achieved, but service continuity deteriorates when the server fails
Solution Approach 1:
Each robot is equipped with a processor that can independently execute task allocation algorithms. The robot autonomously receives context data from sensors and other robots, identifies applicable contexts, and determines its own task without requiring server intervention, thereby maintaining service continuity during server failures.
Solution Approach 2:
The centralized task allocation function is segmented and distributed to individual robots. Each robot maintains local context data and task allocation capabilities, transforming the monolithic server-based system into distributed autonomous units that can operate independently.
2Reliability
If multiple robots operate independently without a management server, then service continuity is improved, but task allocation efficiency deteriorates
Solution Approach 1:
Robots continuously exchange context data with each other through communication interfaces. This feedback mechanism allows robots to maintain awareness of the environment and other robots' states, enabling efficient collaborative task allocation without centralized coordination.
Solution Approach 2:
Context data is pre-collected and stored in each robot's memory before task allocation is needed. By maintaining ready-to-use context information about the environment and other robots, the system enables rapid task determination when required, improving response efficiency.
3Adaptability or versatility
If robots share context data frequently, then collaborative task allocation is improved, but communication overhead increases
Solution Approach 1:
Instead of continuously exchanging all context data, robots share only the specific context data that is currently relevant to task allocation. The processor determines which context data to share based on the current situation, reducing unnecessary communication while maintaining collaborative effectiveness.
Data Source
AI summary
A robot is provided. The robot includes a sensor, a driver, a communication interface, and a processor configured to acquire first context data through the sensor, receive second context data acquired by at least one other robot through the communication interface, identify at least one context data of the first context data and the second context data based on a collaboration scenario of the robot and the other robot, input the identified at least one context data to a predetermined task allocation algorithm related to the collaboration scenario to acquire task information corresponding to the robot, and control the driver based on the acquired task information.