Mobile Robot Localization Using Vector Field Sensors
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Solution Overview
Problem
Existing methods for simultaneous localization and mapping (SLAM) of mobile robots in environments without specific indicators, particularly when using a single sensor, result in insufficient updating of node information, leading to position errors that worsen at higher speeds.
Innovation Solution
A method utilizing multiple vector field sensors to acquire relative and absolute coordinates, defining virtual cells with nodes, and updating position information to estimate new node positions while determining previous node positions, with error compensation and weight calculation for accurate localization and mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single sensor is used to create a vector field for localization and mapping, then the device complexity is reduced, but the position accuracy deteriorates due to insufficient updating of node information
Solution Approach 1:
The patent combines multiple vector field sensors to create a more robust localization and mapping system. By merging the data from multiple sensors, the system achieves better position accuracy and more frequent updating of node information, resolving the contradiction between device complexity and measurement precision.
Solution Approach 2:
The system implements continuous feedback by constantly updating node information based on sensor measurements. This feedback mechanism ensures that position accuracy is maintained and improved over time, allowing the system to correct errors and adapt to changing environmental conditions.
2Productivity
If the mobile robot moves at high speed, then the productivity is improved, but the position accuracy deteriorates due to cumulative errors in localization and mapping
Solution Approach 1:
The patent ensures continuous updating of position information and node data during robot movement. This continuous action allows the system to maintain accuracy even at high speeds by constantly correcting cumulative errors through real-time sensor feedback and node information updates.
Solution Approach 2:
The system uses continuous feedback from multiple vector field sensors to detect and correct cumulative position errors. This feedback mechanism enables the robot to maintain high-speed movement while preserving localization accuracy by constantly comparing expected positions with actual sensor measurements.
3Device complexity
If discrete landmarks are used for localization, then the device complexity is reduced, but the measurement precision deteriorates compared to continuous vector field methods
Solution Approach 1:
The patent transforms the localization approach from discrete landmarks to a continuous vector field representation. By changing the parameter space from discrete points to continuous fields, the system achieves higher measurement precision while maintaining manageable device complexity through the use of standardized sensor arrays.
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 significantly reduces position errors in localization and mapping, enabling more accurate SLAM by using multiple vector field sensors to create a continuous vector field, improving accuracy and reducing errors even at higher speeds.
Implementation Method 1
acquiring an absolute coordinate in the movement space by detecting at least one of intensity and direction of a signal using a plurality of vector field sensors
Data Source
AI summary
A method of localization and mapping of a mobile robot may reduce position errors in localization and mapping using a plurality of vector field sensors. The method includes acquiring a relative coordinate in a movement space using an encoder, acquiring an absolute coordinate in the movement space by detecting intensity and direction of a signal using vector field sensors, defining a plurality of virtual cells on a surface of the movement space such that each of the cells has a plurality of nodes having predetermined positions, and updating position information about the nodes of the cells based on the relative coordinate acquired through the encoder and the absolute coordinate acquired through the vector field sensors and implementing localization and mapping in the movement space in a manner that position information of a new node is estimated while position information of a previous node is determined.