Autonomous Robot Parameter Mapping for Adaptive Task Planning
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Solution Overview
Problem
Current robots used for household, commercial, and industrial tasks, such as vacuuming, wiping, and mowing, are often time-consuming and require complex pre-configurations, which can be inefficient and labor-intensive.
Innovation Solution
An autonomous device equipped with spatial sensors for generating location information and additional sensors for determining further parameters, along with an electronics unit that creates a parameter map by connecting location information with sensor data, allowing for optimized task performance by adjusting based on statistical information and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robots perform household, commercial, and industrial tasks using traditional methods, then tasks can be completed, but task performance is time-consuming and requires complex pre-configurations
Solution Approach 1:
The system performs preliminary actions by creating a parameter map before task execution. The electronics unit generates a map of the area and identifies parameters such as dirt distribution, obstacles, and high-traffic zones in advance. This preliminary mapping allows the robot to optimize its cleaning path and intensity without time-consuming pre-configurations by the user, directly improving productivity while reducing setup time.
Solution Approach 2:
The robot autonomously extracts statistical information from the parameter map and adjusts its own performance without human intervention. It automatically identifies areas requiring more frequent cleaning, optimizes its route planning, and adapts cleaning intensity based on detected parameters. This self-service capability eliminates the need for complex pre-configurations and reduces task completion time significantly.
2Productivity
If robots use complex pre-configurations and settings, then task performance can be optimized, but the system becomes more complex and requires more setup time
Solution Approach 1:
The robot autonomously extracts statistical information from the parameter map and adjusts its own performance parameters without human intervention. It automatically determines cleaning intensity, path optimization, and area prioritization based on detected parameters like dirt distribution and traffic patterns. This self-service capability eliminates the need for complex pre-configurations while maintaining optimized task performance.
Solution Approach 2:
The system dynamically changes operational parameters based on statistical information extracted from the parameter map. The electronics unit adjusts cleaning intensity, speed, and path planning parameters in real-time based on detected conditions such as dirt concentration, obstacle density, and area importance. This adaptive parameter adjustment achieves optimized performance without requiring complex static pre-configurations.
3Reliability
If robots perform thorough cleaning of all areas, then cleaning quality is maintained, but task duration increases significantly
Solution Approach 1:
The system applies different cleaning qualities to different areas based on statistical information from the parameter map. High-traffic areas and zones with higher dirt accumulation receive more intensive and frequent cleaning, while low-traffic areas receive minimal cleaning. This localized quality adjustment maintains overall cleaning reliability while significantly reducing total task duration by avoiding uniform thorough cleaning of all areas.
Solution Approach 2:
The robot performs partial cleaning actions focused on areas that statistically require more attention. Instead of uniformly cleaning all areas to the same standard, it concentrates cleaning resources on high-priority zones identified through parameter analysis, accepting that some low-priority areas receive less intensive treatment. This approach maintains acceptable cleaning quality while reducing overall task duration.
Data Source
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AI summary
An autonomous device (110) and a method for controlling an autonomous device are disclosed. The autonomous device (110) is configured for performing at least one task, selected from a household task, a commercial task and an industrial task and comprises at least one spatial sensor (112) for generating location information; at least one further sensor (114) for determining at least one further parameter; at least one task unit (124) arranged to perform the household and/or commercial and/or industrial task; at least one electronics unit (126) configured to generate a map (128) using the location information and further configured for connecting the further parameter to the location information and for adding the location of the further parameter to the map (128), thereby creating a parameter map containing location- correlated values of the at least one further parameter.