Robot Sensor Assembly Configuration via Spatial Coverage Optimization
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Solution Overview
Problem
Analyzing and optimizing sensor assembly configurations for semi-autonomous robots is complex due to the variety of environmental conditions and object types, requiring reliable environment perception functions while ensuring E/E safety, which is challenging with conventional engineering methods that lack formalization and automation.
Innovation Solution
A model-based method that subdivides the environment into spatial segments, determines individual component performances and sensor assembly requirements, and generates a linear optimization function to assess and optimize the sensor configuration, allowing for automated identification of gaps and reduction of unnecessary components, thereby ensuring efficient and reliable environment perception.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional engineering methods are used to analyze sensor assembly configurations, then the analysis process is simple and intuitive, but the analysis is time-consuming, lacks automation, and cannot efficiently handle the variety of environmental conditions and object types
Solution Approach 1:
The patent replaces manual conventional engineering analysis methods with an automated computer-implemented optimization approach. The system uses a data processing device to automatically perform sensor assembly analysis by generating and evaluating multiple configurations against predefined requirements, substituting human engineering judgment with algorithmic optimization to dramatically improve analysis productivity.
Solution Approach 2:
The patent transforms the analysis problem into a parameter optimization task by defining sensor assembly configurations in terms of adjustable parameters (sensor types, positions, orientations) and using optimization algorithms to find configurations that satisfy all environmental perception requirements across diverse conditions, enabling systematic exploration of the configuration space.
2Reliability
If the sensor assembly configuration is optimized to cover all environmental conditions and object types, then the environment perception reliability is improved, but the number of sensors and system complexity increase
Solution Approach 1:
The patent segments the environmental conditions and object types into discrete categories and test cases, allowing the optimization algorithm to systematically evaluate sensor assembly performance across each segment. This segmentation enables comprehensive coverage of diverse conditions while maintaining manageable analysis complexity through structured requirement definition.
Solution Approach 2:
The patent employs optimization algorithms that may evaluate more sensor configurations than strictly necessary (excessive action) to ensure that the final selected configuration reliably satisfies all requirements. The optimization process explores the configuration space thoroughly to guarantee reliability, even if this requires analyzing more possibilities than a minimal approach would demand.
3Reliability
If numerous experiments and tests are conducted to analyze E/E safety requirements, then the safety analysis completeness is improved, but the analysis time and resources increase significantly
Solution Approach 1:
The patent performs preliminary definition of E/E safety requirements and integrates them into the optimization framework before the actual sensor assembly analysis. By pre-specifying safety constraints and incorporating them into the optimization criteria, the system avoids time-consuming post-hoc safety analysis and experiments, achieving comprehensive safety evaluation as part of the configuration optimization process itself.
4Ease of operation
If manual analysis methods are used to evaluate sensor assembly configurations, then the method is easy to understand and implement, but it cannot efficiently identify gaps or optimize the configuration
Solution Approach 1:
The patent implements a self-service optimization system where the computer program automatically defines the problem space, generates candidate configurations, evaluates them against requirements, and identifies optimal solutions without requiring manual intervention at each step. The system serves itself by autonomously performing the complete analysis workflow, achieving both ease of operation (once implemented) and high analysis precision through systematic algorithmic evaluation.
Data Source
AI summary
A method for analyzing a sensor assembly configuration of an at least semi-autonomous robot includes determining a plurality of spatial segments that spatially subdivide an environment of the robot. The method further includes determining an available individual component performance of a respective individual component of the sensor assembly configuration in relation to the spatial segments. The method further includes determining a sensor assembly requirement that must be satisfied by the sensor assembly configuration in relation to the spatial segments. A linear optimization function with parameters that include the spatial segments, the individual component performances, and the sensor assembly requirements is generated and then solved according to the method. The solution of the linear optimization function indicates if the environment of the robot is configured to be captured by the sensor assembly configuration in accordance with the sensor assembly requirement. A data processing device in one embodiment executes the method.