Autonomous moving object with radar sensor
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Solution Overview
Problem
Commercially available radar sensors for autonomous mobile robots have limited bandwidth, making it difficult to reliably distinguish between differently textured surfaces, which hinders accurate surface type classification and control decisions.
Innovation Solution
An autonomous moving object equipped with a radar sensor and a surface classifier processor that collects and processes radar reflection data subsets from a common sub-region over time, utilizing amplitude and phase variations to improve surface texture differentiation, enabling more reliable surface classification and control decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a commercially available radar sensor with limited bandwidth is used, then the device complexity and cost are reduced, but the measurement precision and ability to resolve surface texture are insufficient
Solution Approach 1:
The patent transitions from analyzing spatial frequency content (single dimension) to analyzing temporal evolution of radar signal characteristics (time dimension). By collecting radar signals over multiple time points and analyzing how amplitude and phase evolve temporally, the system achieves superior surface texture resolution despite using a bandwidth-limited radar sensor. This temporal dimension adds information that compensates for the limited spatial frequency content.
Solution Approach 2:
The patent changes the analysis parameters from static spatial frequency content to dynamic temporal evolution parameters. Instead of examining the spatial spectrum of a single radar signal, the system analyzes how signal amplitude, phase, and other characteristics change over time as the robot moves. This parameter transformation enables surface texture differentiation using limited bandwidth radar data.
2Measurement precision
If radar signal bandwidth is increased to improve surface resolution, then measurement precision improves, but the sensor cost and complexity increase
Solution Approach 1:
The patent implements continuous radar signal acquisition over time, collecting signals at multiple time points as the robot moves across the surface. This continuous measurement approach allows the system to accumulate temporal information about surface reflections, improving classification reliability without requiring higher bandwidth. The useful action of radar measurement continues uninterrupted, gathering data that reveals surface characteristics through temporal patterns.
Solution Approach 2:
The system uses the temporal evolution patterns of radar signals as feedback to infer surface texture properties. By analyzing how the radar signal characteristics change over time in response to robot movement, the system gains feedback information about surface compliance and texture that would otherwise require higher bandwidth to detect. This feedback mechanism transforms temporal dynamics into surface classification information.
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
The solution allows for more accurate and reliable distinction between various surface types, enhancing the robot's ability to navigate and control operations based on surface conditions, such as differentiating between grass and non-grass surfaces or carpeted and bare floors.
Implementation Method 1
receiving reflected portions of a radar signal transmitted by a radar sensor arranged on the autonomous moving object
Data Source
Figure 1~3b
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Figure 5
AI summary
An autonomous moving object comprising a radar sensor is provided. The radar sensor is configured to, during movement, acquire data sets representing reflections from surface portions located within a distance range, and, at least at a sequence of occasions, illuminate a surface region and acquire a data set representing, for each of a set of distances within said distance range, an amplitude and a phase of reflected radar signals received from surface portions located at said distance. Said surface regions comprise a common sub-region illuminated at each of said occasions. A radar signal processor is configured to receive the data sets acquired at each of said sequence of occasions. The received data sets form a collection of data sets, wherein each data set of said collection comprises a data subset pertaining to said common sub-region. A surface classifier processor is configured to output a classification of a surface type of the surface based on said collection of data subsets.