Mobile Robot Route Planning Around Network Shadow Regions
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Solution Overview
Problem
Existing mobile robots do not consider network circumstances when setting routes, leading to potential disruptions in network shadow regions that can affect their operation and efficiency.
Innovation Solution
A mobile robot equipped with a pre-trained network performance estimation model that determines network shadow regions and updates its route to avoid or pass through these areas, using a processor to control the driver and manage data transmission based on estimated network performance levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the mobile robot sets a route without considering network circumstances, then the route planning is simple and fast, but the robot may enter network shadow regions causing operation disruptions
Solution Approach 1:
The system performs preliminary network performance estimation and identifies network shadow regions before the robot executes the route. By pre-processing network data and marking problematic areas in advance, the robot can avoid disruptions without adding complexity to real-time route planning execution.
Solution Approach 2:
A network performance estimation model acts as an intermediary between the route planning system and the actual network environment. This model predicts network quality in advance, allowing the route planner to make informed decisions without directly dealing with complex real-time network variations.
2Reliability
If the mobile robot avoids network shadow regions by updating the route, then network connection stability is improved, but the route may become longer and take more time
Solution Approach 1:
The system applies partial route updates only when network shadow regions are detected, rather than continuously adjusting the route. By selectively updating only when necessary, the system maintains connection stability without unnecessarily extending the route or execution time.
Solution Approach 2:
The route planning parameters are dynamically adjusted based on network performance predictions. When network shadow regions are identified, the system modifies route parameters to avoid these areas, balancing connection stability with efficient path selection.
3Reliability
If the mobile robot collects network performance information in advance, then disruption prevention is improved, but the data collection process consumes additional energy and time
Solution Approach 1:
Network performance information is collected in advance during periods when the robot is stationary or moving slowly, rather than continuously during high-speed operation. This preliminary data collection enables disruption prevention while minimizing energy consumption during critical movement phases.
Solution Approach 2:
The network performance estimation model continuously processes available data in the background without interrupting the robot's primary movement function. By running the estimation process continuously at low computational overhead, the system maintains disruption prevention capability without significant additional energy expenditure.
Data Source
AI summary
A mobile robot is disclosed. The mobile robot may include a wireless transceiver, a driver, and a processor. The mobile robot may execute an artificial intelligence (AI) algorithm and/or a machine learning algorithm, and perform communications with other electronic devices in a 5G communication network. Accordingly, user convenience can be significantly improved.