Robot Driving Map Using Network Latency Grids and Success Rates
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Solution Overview
Problem
Current robot driving maps do not effectively account for network latency issues, which can cause instability and service failures due to external factors like user connections and modem/router status, and these issues are typically identified only during field tests.
Innovation Solution
A latency-based robot driving map generation method that divides spaces into grids, measures network latency for each grid, calculates success rates through path repetition, and sets path priorities based on latency levels and success rates, updating the map in real time to avoid high-latency areas and improve driving stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robot driving maps are created using traditional obstacle scanning methods, then physical obstacles and reflective surfaces are detected, but network latency issues affecting robot driving stability are not identified until field tests
Solution Approach 1:
The space is divided into multiple grids, and network latency is measured for each grid separately. This segmentation allows the system to identify specific areas with network issues rather than treating the entire environment uniformly, enabling targeted path planning decisions based on local network conditions
Solution Approach 2:
Network latency measurement and map generation are performed before robot driving operations begin. By pre-measuring network conditions and generating a latency-based map in advance, the system proactively identifies problematic areas rather than reacting to network issues during field tests, improving driving stability from the outset
2Reliability
If the robot avoids all areas with network latency, then service failures are reduced, but the robot cannot utilize areas where latency occurs intermittently with acceptable success rates
Solution Approach 1:
Different latency threshold criteria are applied to different grids based on their specific network conditions. High-latency areas receive stricter avoidance criteria, while areas with intermittent but acceptable latency are assigned different priority levels, allowing the robot to selectively operate in these regions based on success rate thresholds
Solution Approach 2:
The system dynamically adjusts path selection priorities based on measured success rates. By continuously monitoring whether service operations succeed in grids with intermittent latency and adjusting the priority parameters accordingly, the robot can safely utilize these areas when conditions are favorable while avoiding them when success rates drop below thresholds
3Productivity
If the robot operates in areas with high network latency, then more service coverage is achieved, but service failures increase due to network instability
Solution Approach 1:
The system measures network latency and calculates service success rates in each grid, then uses this feedback information to dynamically adjust path selection priorities. This closed-loop approach allows the robot to learn from actual performance data and optimize its operating areas, achieving better coverage while maintaining reliability through data-driven decisions
Data Source
AI summary
A latency-based robot driving map generation apparatus includes a network map generator configured to divide a space into grids and measure network latency for each grid to generate a network map with assigned latency levels. The apparatus further includes a success rate calculator configured to calculate success rates by repeated driving operations on paths within grids assigned specific latency levels in the network map. Additionally, a driving path determiner determines a driving path on the network map based on the assigned latency levels and the calculated success rates.