Robot Cluster Scheduling with Plug-In Task and Route Allocation
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Solution Overview
Problem
Existing robot scheduling systems lack support for secondary development and fail to optimally allocate tasks and routes, resulting in low efficiency of task execution.
Innovation Solution
A robot cluster scheduling system with a user layer, intermediate layer, application layer, plug-in layer, and data persistence layer, featuring a processor mapping module, state acquisition module, task scheduling module, and traffic scheduling module, which uses task and traffic planning engines to determine target robots and routes based on task parameters and state data, improving task decomposition and route planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a traditional robot scheduling system with interface layer, service processing layer and access layer is used, then the system structure is simple, but the system does not support secondary development and cannot optimally allocate tasks and routes
Solution Approach 1:
The system is divided into five distinct layers: user layer, intermediate layer, application layer, plug-in layer, and data persistence layer. Each layer has specific functions and can be independently developed and maintained. The plug-in layer specifically enables secondary development through standardized interfaces, allowing new functionalities to be added without restructuring the entire system.
Solution Approach 2:
The intermediate layer acts as a mediator between the user layer and the application layer. It includes a processor mapping module that receives tasks from users and forwards them to the appropriate application layer modules, and a state acquisition module that collects robot state data and makes it available to the application layer, enabling flexible task allocation without direct coupling.
2Productivity
If traditional task allocation methods are used in robot scheduling systems, then the system structure is simple, but task execution efficiency is low due to non-optimal task and route allocation
Solution Approach 1:
The task solving engine dynamically determines the target robot based on real-time task parameters and robot state data. The traffic planning engine dynamically plans optimal routes based on current robot positions and task requirements. This dynamic allocation ensures optimal task execution efficiency while adapting to changing system conditions.
Solution Approach 2:
The state acquisition module continuously collects robot state data (position, status, workload) and feeds it back to the task solving engine and traffic planning engine. This feedback mechanism enables the system to make informed decisions about task allocation and route planning, optimizing task execution efficiency through real-time information.
Data Source
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Figure 2C
AI summary
A robot cluster scheduling system includes a user layer, an intermediate layer, an application layer, a plug-in layer and a data persistence layer. The intermediate layer includes a processor mapping module and a state acquisition module. The application layer includes a task scheduling module and a traffic scheduling module. The plug-in layer includes a task solving engine and a traffic planning engine. The task solving engine is configured to determine a target robot according to a parameter of a task and state data. The traffic planning engine is configured to determine a target route. The task solving engine and the traffic planning engine each provide an application programming interface (API).