Mobile Robot Control Network With Predictive QoS Allocation
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Solution Overview
Problem
Existing mobile robot control systems face challenges in maintaining deterministic network performance and efficient resource allocation, particularly due to network congestion and quality of service fluctuations, which affect productivity and require better coordination between fleet management and network management systems.
Innovation Solution
A control network architecture that separates the responsibilities of path planning for mobile robots and network resource allocation, allowing the fleet management system to generate predictive network resource requests and the network management system to adapt resource allocation based on these requests, ensuring efficient resource use and quality of service across the facility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If more complex wireless networks are applied to meet increasing needs for reliability, latency control, security and coverage, then network performance is improved, but device complexity increases
Solution Approach 1:
The system divides network management into two independent modules: path planning (handled by FMS) and network resource allocation (handled by NMS). This segmentation allows each module to specialize in its function, improving overall network performance while managing complexity through separation of concerns.
Solution Approach 2:
A predictive network resource request mechanism acts as an intermediary between the FMS and NMS. The FMS generates predictive requests based on path planning, which the NMS uses to proactively allocate resources. This intermediary layer coordinates the two modules without requiring direct complex interaction, maintaining reliability while controlling system complexity.
2Extent of automation
If route selection is delegated to each mobile robot, then robot autonomy is improved, but fleet management efficiency deteriorates
Solution Approach 1:
The central FMS acts as an intermediary that receives path planning requests from individual robots, processes them collectively considering fleet-wide conditions, and returns optimized paths. This maintains robot autonomy in initiating movements while improving fleet efficiency through centralized coordination and predictive resource allocation.
Solution Approach 2:
The system performs preliminary path planning and network resource allocation before robots execute their movements. By planning routes and allocating resources in advance based on predictive requests, the system optimizes fleet management efficiency while preserving robot autonomy during actual execution.
3Productivity
If in-advance network resource allocation is performed, then network resource utilization is improved, but adaptability to dynamic conditions deteriorates
Solution Approach 1:
The system dynamically adjusts network resource allocation based on real-time conditions. The FMS continuously generates updated predictive network resource requests as robots move and conditions change, allowing the NMS to adapt resource allocation dynamically while maintaining high utilization through continuous predictive planning.
Solution Approach 2:
The system implements a feedback loop where the FMS monitors robot positions and path execution, generates updated predictive resource requests, and the NMS adjusts allocation accordingly. This feedback mechanism enables both efficient resource utilization through predictive allocation and adaptability to dynamic conditions through continuous updates.
Data Source
Figure 1
Figure 2~3
AI summary
A control network (100) for supporting one or more mobile robots (130) operable in a facility comprises: a fleet management system (FMS, 110) authorized to perform path planning and path execution for the mobile robots; and a network management system (NMS, 120) authorized to configure and perform resource allocation in an access network (125), which is operable to provide the mobile robots with wireless connectivity in the facility. The respective authorities of the FMS and NMS are mutually exclusive, and the FMS is configured to generate, on the basis of the path planning, a predictive network resource request and share this with the NMS. A vertical application layer (VAL) server is configured to generate a network resource request which specifies a future location of said one or more VAL UEs. A network resource management server is configured to generate a QoS forecast for one or more VAL UEs.