ISCC Closed-Loop AGV Control for Congestion and Collision Prevention
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Solution Overview
Problem
Existing closed-loop control systems for intelligent machines, such as AGVs, face challenges in large-scale deployments due to high resource consumption, poor control performance, and increased risks of congestion, collision, and loss of control, which are not adequately addressed by current sensing-control and communication-control collaborations.
Innovation Solution
A method and device based on Integrated Sensing, Communication, and Control (ISCC) for closed-loop control of multiple intelligent machines, which includes active and passive control decisions based on sensor-sensed motion state observations, adapting the motion trajectory and speed of AGVs to environmental conditions, reducing resource consumption and improving control stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of AGVs and working speed increase to meet production demands, then productivity improves, but the failure rate and risk of loss of control increase
Solution Approach 1:
The system segments the control architecture into edge computing nodes distributed across the factory, with each node managing a subset of AGVs. This segmentation reduces the control burden on any single node and enables localized decision-making, thereby maintaining reliability as the system scales to support higher productivity with more AGVs.
Solution Approach 2:
The system dynamically adjusts communication resources and control parameters based on real-time factory conditions. When productivity demands increase and more AGVs are deployed, the system dynamically allocates communication channels and adjusts control frequencies to maintain stability and prevent loss of control, rather than using fixed parameters.
2Manufacturing precision
If existing closed-loop control schemes are used with fixed paths, then manufacturing precision is maintained, but adaptability to new paths deteriorates and extensive infrastructure modifications are required
Solution Approach 1:
The system employs dynamic path planning algorithms that allow AGVs to adapt to new paths without infrastructure modifications. The control parameters and trajectories are dynamically adjusted based on real-time sensing and edge computing, enabling the system to maintain manufacturing precision while being highly adaptable to changing production requirements and new paths.
Solution Approach 2:
The system uses continuous feedback from sensors and edge computing nodes to monitor AGV position and trajectory deviations. This feedback loop enables real-time corrections to maintain manufacturing precision even when AGVs operate on new or modified paths, eliminating the need for extensive infrastructure changes.
3Device complexity
If existing closed-loop control schemes focus on unidirectional link parameters, then device complexity is reduced, but the ability to meet efficient information flow interaction requirements deteriorates
Solution Approach 1:
The system segments the communication architecture into multiple edge computing nodes distributed throughout the factory, each handling local AGV control and sensing data. This segmentation reduces the complexity of any single control node while enabling efficient information flow interaction across the entire system, as each node processes data locally before coordinating with others.
Solution Approach 2:
The edge computing nodes are designed with multi-functionality, handling sensing data processing, control command generation, communication management, and coordination with other nodes. This universal design reduces overall system complexity by consolidating multiple functions into standardized nodes while maintaining efficient information flow for high-productivity operations.
4Productivity
If the number of AGVs increases in large-scale deployment, then productivity improves, but communication congestion and collision risks increase dramatically
Solution Approach 1:
The system segments the factory into multiple edge computing zones, each managing a subset of AGVs and their communication channels. This segmentation reduces communication congestion by localizing data processing and control commands, preventing the exponential increase in communication overhead that would occur with centralized control of large numbers of AGVs.
Solution Approach 2:
The system dynamically adjusts communication resource allocation and AGV scheduling based on real-time factory conditions. When the number of AGVs increases, the system dynamically assigns communication channels, adjusts transmission priorities, and coordinates AGV movements to prevent collisions and communication congestion, enabling high productivity with large-scale deployment.
Data Source
AI summary
The present invention provides a method and device based on ISCC for closed-loop control of multiple intelligent machines, relating to the technical field of ISCC, the method including: in response to determining that a first mobile terminal meets a first condition, receiving a first control instruction sent by a communication network device, the first control instruction is used to instruct to adjust a motion trajectory and a motion speed of the first mobile terminal within a target motion period; in response to determining that the first mobile terminal does not meet the first condition, adjusting the motion trajectory and the motion speed of the first mobile terminal within the target motion period based on a sensor-sensed first motion state observation value of the first mobile terminal and/or a sensor-sensed second motion state observation value of a second mobile terminal.