Swarm Robot Task Claiming to Avoid AGV Server Overload
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Solution Overview
Problem
The existing Auto Guided Vehicle (AGV) scheduling decision system is overloaded due to AGVs' inability to communicate with each other, leading to centralized decision-making that can result in server overload and potential system crashes if the central control server fails.
Innovation Solution
A distributed robot scheduling decision method where swarm robots communicate with each other to receive and transmit task packs, making decisions based on claiming decision variables to execute suitable tasks independently, thereby reducing server overload and enabling decentralized decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized control server schedules all AGVs, then unified scheduling decision is achieved, but server overload occurs at high possibility
Solution Approach 1:
The patent segments the centralized scheduling function into distributed decision-making units. Each AGV is equipped with independent scheduling capabilities, dividing the original centralized control task into multiple autonomous decision-making entities. This segmentation eliminates the server overload problem by distributing the computational burden across multiple AGVs rather than concentrating it in a single central server.
Solution Approach 2:
The patent implements self-service by enabling each AGV to make its own scheduling decisions autonomously. Each AGV evaluates task packs and makes execution decisions based on its own capabilities and current state, without requiring constant central server intervention. This self-service mechanism reduces dependency on the central server and prevents overload while maintaining effective scheduling.
2Device complexity
If AGVs cannot communicate with each other, then simple individual control is maintained, but system robustness decreases when central server fails
Solution Approach 1:
The patent introduces task packs as intermediary carriers of scheduling information. Instead of requiring direct complex communication between AGVs or constant server-AGV communication, the task pack serves as a standardized intermediary that contains all necessary task information. AGVs can independently process these task packs, and the server only needs to distribute task packs rather than manage complex real-time communications, thus reducing communication complexity while improving robustness.
3Reliability
If central control server makes all decisions, then unified scheduling is achieved, but execution efficiency decreases due to communication overhead
Solution Approach 1:
The patent applies preliminary action by pre-packaging task information into comprehensive task packs that contain all necessary details for execution decisions. The server prepares and distributes these task packs in advance, allowing AGVs to make decisions locally without requiring continuous real-time communication with the server. This preliminary preparation of task information maintains scheduling unity while significantly reducing communication overhead and improving execution efficiency.
Data Source
AI summary
Provided is a distributed robot scheduling decision method. The method includes: a task pack including at least one task is received, and the task pack is transmitted to other robots in swarm robots (S10); a decision is made according to a claiming decision variable to claim a task suitable for execution in the task pack (S11); and the task suitable for execution is executed (S12). In such a manner, swarm robots may communicate with one another for task transmission and make decisions according to claiming decision variables to claim tasks suitable for execution in the task pack for execution. Therefore, a technical effect that the swarm robots may make decisions independently rather than in centralized decision and central control decision manners to effectively avoid overloading a server at a high possibility is achieved, and moreover, a technical effect of intelligently selecting tasks for execution to improve the execution efficiency is achieved.