Robotic Semantic Identification Using Retro-Reflective Markers
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Solution Overview
Problem
Robotic systems face challenges in accurately identifying and locating objects of interest and themselves within environments, particularly due to blind spots and mis-localization, which can lead to unsafe navigation and reduced detection confidence.
Innovation Solution
The implementation of a system using a mobile robot equipped with sensors, a computer, and retro-reflective markers that allow for semantic identification of objects and locations by combining shape and intensity data, enabling precise placement and high-confidence detection even in areas outside the sensor's field of view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot uses conventional sensor-based detection, then the detection process is simple, but the detection confidence is reduced and blind spots occur
Solution Approach 1:
The patent introduces retro-reflective markers as intermediary objects that mediate between the sensor and the target object. These markers reflect sensor light back to the detector, enabling reliable detection of objects that would otherwise be in blind spots or partially occluded, thereby increasing detection confidence without requiring complex system changes
Solution Approach 2:
The patent uses retro-reflective markers with specific optical properties that change how light is reflected back to the sensor. The markers appear bright or have distinct intensity characteristics in sensor data, allowing the system to distinguish marked objects from the background and提高 detection reliability
2Reliability
If the robot operates without semantic identification, then the navigation is simpler, but the robot cannot avoid danger areas or mis-localization
Solution Approach 1:
The patent applies retro-reflective markers to objects and locations in advance, before the robot encounters them. This preliminary marking enables the robot to identify danger areas, locate objects of interest, and correct mis-localization issues without requiring complex real-time decision-making, thereby improving navigation safety
Solution Approach 2:
The patent creates a semantic map that copies and represents the physical environment with marked locations and objects. This digital representation allows the robot to understand its location and identify danger areas without requiring complex physical sensors or processors, improving navigation safety through information processing
3Measurement precision
If the robot uses only shape-based identification, then the system is simpler, but the identification accuracy is reduced when objects are partially occluded
Solution Approach 1:
The retro-reflective markers serve as intermediaries that provide additional identification information beyond shape. When an object is partially occluded, the marker's distinctive reflective properties allow the sensor to detect and identify the object based on intensity patterns rather than complete shape information, thereby improving identification accuracy
Solution Approach 2:
The patent changes the identification parameters from relying solely on shape geometry to including optical intensity characteristics of retro-reflective markers. This parameter expansion allows the system to identify objects even when shape information is incomplete due to occlusion, improving measurement precision
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 solution enhances the robot's ability to accurately identify and locate objects and itself, preventing navigation into danger areas and improving detection confidence, especially when objects are partially occluded or above the sensor's range, thereby ensuring safer and more precise operation.
Implementation Method 1
the object of interest marked with a marker at one or more of an approximate height and an approximate field of view of the sensor, the sensor generating data describing the object of interest
Data Source
AI summary
A system includes: a mobile robot comprising a sensor, the robot further comprising a computer, the robot operating in an environment; a server operably connected to the robot via a communication system, the server configured to manage the robot; a controller operably connected to the robot, the controller operably connected to the server, the controller configured to control the robot; and an object of interest marked with a marker at one or more of an approximate height and an approximate field of view of the sensor, the sensor generating data describing the object of interest, the computer configured to identify one or more of the object of interest and the location using one or more of a shape of the object of interest and an intensity of data describing the object of interest.