Autonomous Robot Parameter Mapping for Adaptive Cleaning Paths
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Solution Overview
Problem
Existing robots used for household, commercial, and industrial tasks, such as vacuuming and mowing, often require complex configurations and are time-consuming, with many tasks involving expensive pre-settings and inefficient performance.
Innovation Solution
An autonomous device equipped with spatial sensors for location information and additional sensors for determining task-related or environmental parameters, which generates a parameter map to optimize task performance by identifying areas needing more frequent or intense cleaning, adjusting its path, and providing recommendations for environmental improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional robots perform household tasks using predetermined movement procedures, then basic cleaning operations can be executed, but the device complexity increases and task performance becomes time-consuming
Solution Approach 1:
The robot performs tasks autonomously using statistical information about the environment generated by its own sensor data. The system self-learns patterns of dirt accumulation, obstacle locations, and cleaning efficiency without requiring external programming or complex pre-configurations, enabling it to optimize its own performance over time
Solution Approach 2:
The robot generates statistical information and creates maps of the environment in advance during initial explorations. This preliminary data collection about dirt patterns, obstacles, and surface characteristics enables the robot to make informed decisions during subsequent cleaning operations, improving task efficiency without requiring complex real-time processing
2Productivity
If robots use predetermined movement procedures for cleaning, then basic tasks can be completed, but loss of time increases due to inefficient path planning
Solution Approach 1:
The robot continuously collects sensor data during cleaning operations and updates its statistical information and maps in real-time. This feedback loop allows the system to learn from its cleaning performance, identify areas that require more attention, and optimize its path planning for subsequent operations, progressively reducing task completion time
Solution Approach 2:
The robot's movement patterns and cleaning strategies are dynamically adjusted based on statistical information about the environment. Rather than following fixed predetermined paths, the system adapts its behavior to match the actual conditions discovered through sensor data, optimizing cleaning efficiency for each specific area and reducing overall task time
3Reliability
If robots perform comprehensive cleaning operations, then thorough task completion is achieved, but loss of time increases due to repeated cleaning of already clean areas
Solution Approach 1:
The robot applies different cleaning strategies to different areas based on statistical information about local conditions. Areas identified as consistently dirty receive more frequent and intensive cleaning, while already clean areas are visited less often or skipped entirely. This localized approach maintains high cleaning thoroughness where needed while eliminating redundant operations in clean areas
Data Source
AI summary
An autonomous device and method for controlling an autonomous device are disclosed. The autonomous device is configured for performing at least one task, selected from a household task, a commercial task and an industrial task. The autonomous device comprises at least one spatial sensor for generating location information and at least one further sensor for determining at least one further parameter. The autonomous device further comprises at least one task unit arranged to perform the household and/or commercial and/or industrial task, and at least one electronics unit, wherein the electronics unit is configured to generate a map using the location information, wherein the electronics unit further is configured for connecting the further parameter to the location information and adding the location of the further parameter to the map, thereby creating a parameter map containing location-correlated values of the at least one further parameter.