Mobile Robot Localization Using Wireless Signal Coverage Maps
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Solution Overview
Problem
Integrating the autonomous functionality of mobile robots with IoT connectivity poses challenges due to unpredictable environment conditions and the need for accurate localization and navigation in household settings, where existing technologies struggle to effectively combine sensor data and wireless communication signals for precise positioning.
Innovation Solution
A method for operating mobile robots that involves navigating and mapping their environment using both localization sensors and wireless communication signals, constructing 2D or 3D signal coverage maps to determine their location and that of IoT devices, and transmitting audio/visual information, while also identifying interfering communications and altering communication channels as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mobile robots use traditional localization methods (e.g., SLAM, sensor-based navigation) in household environments, then they can achieve autonomous navigation, but their localization accuracy deteriorates due to unpredictable environment conditions and signal interference
Solution Approach 1:
The patent combines multiple localization approaches by integrating sensor-based localization data with wireless communication signal data. The robot simultaneously processes information from its onboard sensors and Wi-Fi signals from surrounding electronic devices, merging these data sources to achieve more accurate and reliable localization than either method could provide alone in unpredictable household environments.
Solution Approach 2:
The patent introduces wireless communication signals as an intermediary element for localization. Instead of relying solely on direct sensor measurements that may fail in certain conditions, the system uses Wi-Fi signals from electronic devices as intermediate reference points to determine the robot's position, thereby improving reliability in environments where traditional sensor-based methods struggle.
2Adaptability or versatility
If mobile robots integrate IoT connectivity and wireless communication for navigation, then they can access more environmental data, but the complexity of combining and processing multiple data sources increases
Solution Approach 1:
The patent makes the robot's localization system multi-functional by enabling it to process both traditional sensor data and wireless communication data through a unified framework. The same processor that handles sensor fusion also processes Wi-Fi signal strength data and electronic device location information, allowing the system to adapt to different environmental conditions without requiring separate specialized processing paths.
Solution Approach 2:
The patent changes the parameters used for localization by incorporating wireless signal strength measurements alongside traditional sensor measurements. The system dynamically adjusts the weight and importance of different data sources based on their reliability in current conditions, transforming the localization problem from a single-parameter task to a multi-parameter optimization problem that improves adaptability.
3Measurement precision
If mobile robots map wireless communication signals to improve localization, then they can achieve better positioning accuracy, but the time and computational resources required for signal mapping increase
Solution Approach 1:
The patent performs preliminary mapping of wireless communication signals and electronic device locations during periods when the robot is stationary or performing other tasks. By pre-establishing the relationship between Wi-Fi signal strengths and physical locations, the system creates a reference map that can be quickly consulted during navigation, avoiding the need to perform comprehensive signal mapping in real-time and thus reducing mapping time.
Solution Approach 2:
The patent implements partial signal mapping by focusing on mapping only the most relevant wireless signals and electronic devices in the current operational area, rather than attempting to map all possible signals in the entire environment. This selective approach reduces the computational burden and time required for mapping while still achieving sufficient positioning accuracy for navigation tasks.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the robot's ability to navigate and localize itself within the environment, improves IoT device communication, and resolves issues related to signal interference, leading to more efficient and accurate autonomous operation in household settings.
Implementation Method 1
acquiring, via at least one wireless receiver of the mobile robot, wireless communication signals responsive to operating the drive to navigate the mobile robot in the operating environment, where the wireless communication signals are transmitted by at least one electronic device that is local to the operating environment
Implementation Method 2
detecting, via at least one localization sensor of the mobile robot, localization data responsive to operating the drive to navigate the mobile robot in the operating environment. The localization data is detected in a surface mapping routine to record 2-dimensional occupancy data representing locations of the operating environment and/or poses of the mobile robot thereon
Implementation Method 3
determining a location of the mobile robot in the operating environment based on correlation of the localization data detected by the at least one localization sensor and the wireless communication signals acquired by the at least one wireless receiver
Data Source
Figure 1A
Figure 1B
Figure 2A~2B
AI summary
A method of operating a computing device includes receiving occupancy data for an operating environment of a mobile robot based on localization data detected by at least one localization sensor of the mobile robot responsive to navigation thereof in the operating environment, and receiving signal coverage data for the operating environment based on wireless communication signals acquired by at least one wireless receiver of the mobile robot responsive to navigation thereof in the operating environment. The wireless communication signals are transmitted by at least one electronic device that is local to the operating environment. The method further includes generating a map indicating coverage patterns of the wireless communication signals at respective locations in the operating environment by correlating the occupancy data and the signal coverage data. Related methods, mobile robots, and user terminals are also discussed.