Robot Cluster Scheduling with API-Based Task and Traffic Engines
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Solution Overview
Problem
Existing robot scheduling systems lack support for secondary development, fail to optimally allocate tasks and routes, resulting in low task execution efficiency.
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 parses tasks and traffic planning requests, determines target robots and routes, and decomposes tasks into subtasks and routes using task and traffic planning engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a traditional robot scheduling system is used, then the system structure is simple, but the task execution efficiency is low due to lack of optimal task allocation and route planning
Solution Approach 1:
The scheduling system is divided into multiple independent modules including processor mapping module, state acquisition module, task scheduling module, and traffic scheduling module. Each module handles specific functions independently, allowing the system to achieve optimal task allocation and route planning while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The task solving engine and traffic planning engine act as intermediary components between the scheduling modules and robot control systems. These engines receive task information, perform optimization calculations, and generate optimized task assignments and routes, thereby improving task execution efficiency without requiring direct complex interactions between all system components.
2Productivity
If the robot control system directly sends task instructions without optimization, then the system operation is simple, but the task allocation and route planning are not optimal
Solution Approach 1:
The task solving engine performs preliminary optimization of task allocation before instructions are sent to robots. It analyzes task parameters, robot states, and constraints in advance to determine the optimal robot assignment and task sequence, ensuring efficient task allocation without requiring complex real-time decision-making during execution.
Solution Approach 2:
The traffic planning engine enables robots to autonomously plan their own routes by providing them with traffic planning requests containing task information. Each robot can independently determine its optimal path based on the provided parameters and current traffic conditions, reducing the operational complexity of centralized route management while improving task allocation efficiency.
3Productivity
If a multi-layer scheduling system with engines is implemented, then the task execution efficiency improves, but the system complexity increases
Solution Approach 1:
The task solving engine and traffic planning engine are designed as universal components that can handle multiple types of tasks and robots through standardized interfaces. By implementing multi-functionality, the system achieves improved task execution efficiency across diverse scenarios without proportionally increasing system complexity, as the same engines serve multiple purposes.
Solution Approach 2:
The state acquisition module continuously monitors robot states and feeds this information back to the task solving engine and traffic planning engine. This feedback mechanism enables dynamic optimization of task allocation and route planning based on real-time conditions, improving task execution efficiency while maintaining system manageability through automated closed-loop control rather than complex manual intervention.
Data Source
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).