Optical Identifier Navigation for Mobile Robots Across Production Sites
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Solution Overview
Problem
Existing navigation systems for autonomous mobile robots in large environments, such as production or maintenance facilities, face limitations in navigational range and reliability, particularly when transitioning between different regions like buildings or levels, leading to inefficiencies and high workload in tasks like surface inspection and manufacturing.
Innovation Solution
A navigation system utilizing optical identifiers, such as QR codes, distributed at fixed locations within the environment, enabling the autonomous mobile robot to determine its real-time location and orientation through optical sensors, combined with triangulation and redundancy checks using LiDAR scanners for enhanced accuracy and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LiDAR scanners are used for navigation, then navigation within limited ranges is enabled, but navigation between different regions and buildings is problematic
Solution Approach 1:
The environment is segmented into multiple regions, each containing optical identifiers at fixed locations. The system divides the navigation task into local identification (using optical identifiers in current region) and regional transition (moving between regions), enabling both local precision and global coverage across buildings and outdoor areas
Solution Approach 2:
Optical identifiers serve as intermediary markers between the robot and the environment. These identifiers are distributed throughout the environment at fixed locations and provide reference points that enable the robot to determine its position and navigate between different regions, including transitions between buildings and outdoor areas
2Measurement precision
If manual marking is performed to locate anomalies, then accurate positioning is achieved, but workload increases significantly
Solution Approach 1:
The system enables self-service navigation and positioning through automatic detection of optical identifiers. The robot autonomously determines its location and orientation by detecting and decoding optical identifiers in its environment, eliminating the need for manual marking operations while maintaining accurate anomaly positioning
Solution Approach 2:
The manual mechanical marking process is replaced with an optical detection system. The robot uses optical sensors to detect and decode optical identifiers, automatically determining its position without physical interaction with the inspected surface, thereby increasing inspection efficiency while maintaining positioning accuracy
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
Enables reliable and efficient navigation of autonomous mobile robots in large environments, reducing manual intervention and increasing operational efficiency by accurately determining positions and navigating between regions, including indoor and outdoor transitions.
Implementation Method 1
detect visible optical identifiers of the plurality of optical identifiers, which are within a field of view of the at least one optical sensor
Data Source
AI summary
A navigation system for navigating an autonomous mobile robot in an environment is provided. The navigation system includes at least one optical sensor attached to the autonomous mobile robot, a controller in communication with the at least one optical sensor, and a plurality of optical identifiers distributed within the environment at fixed locations and detectable by the at least one optical sensor. Each of the plurality of optical identifiers encodes a location within the environment. The controller is configured to obtain pictures of the environment via the at least one optical sensor, detect visible optical identifiers of the plurality of optical identifiers, which are within a field of view of the at least one optical sensor, decode the visible optical identifiers, and navigate the autonomous mobile robot based on real-time localizations of the autonomous mobile robot within the environment using the decoded visible optical identifiers.