Robotic Cleaner Distance Sensing for Velocity-Based Localization
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Solution Overview
Problem
Robotic cleaners face challenges in accurately estimating their velocity while navigating surfaces, particularly due to limitations in existing navigation systems that fail to effectively utilize distance sensors for precise localization and obstacle detection.
Innovation Solution
The use of one or more distance sensors, such as time of flight sensors, to measure detection distances and track abnormalities, allowing the robotic cleaner to estimate its velocity by calculating the time it takes to travel over a known distance, which can be combined with other localization data for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional navigation systems are used for velocity estimation, then the system complexity is low, but the measurement precision of velocity is insufficient
Solution Approach 1:
The patent combines distance sensor data with wheel encoder data to estimate velocity. The distance sensors measure the rate of change in distance to obstacles, while wheel encoders measure wheel rotation. By merging these two measurement sources through sensor fusion algorithms, the system achieves more accurate velocity estimation than either sensor could provide alone, resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
The patent introduces an intermediary processing layer that fuses data from multiple sensors (distance sensors and wheel encoders) to derive velocity. This intermediary fusion mechanism allows the system to leverage complementary information from different sensor types, improving velocity estimation accuracy without requiring a complete redesign of the navigation system.
2Measurement precision
If distance sensors are added to improve localization accuracy, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The patent makes the distance sensors serve multiple functions: they are used for obstacle detection, localization, and velocity estimation. By extracting multiple useful signals from the same sensor hardware, the system achieves improved localization accuracy and velocity estimation without proportionally increasing device complexity, as the same sensors contribute to multiple navigation tasks simultaneously.
3Reliability
If multiple sensors are integrated for better velocity estimation, then the reliability improves, but the ease of operation deteriorates
Solution Approach 1:
The patent implements self-service through automatic sensor calibration and fusion parameter optimization. The system automatically adjusts fusion weights and calibration parameters based on environmental conditions and sensor performance, reducing the need for manual configuration and maintenance. This maintains high reliability through adaptive optimization while preserving ease of operation by eliminating complex manual setup requirements.
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 method enhances the robotic cleaner's ability to localize itself within a map and avoid non-traversable obstacles, leading to more accurate velocity estimation and adaptive navigation.
Implementation Method 1
The use of one or more distance sensors, such as time of flight sensors
Data Source
AI summary
A robotic cleaner may include a body, one or more driven wheels configured to urge the body across a surface to be cleaned, one or more distance sensors disposed at least partially within the body such that the one or more distance sensors face the surface to be cleaned and a processor. The one or more distance sensors may be configured to output a measure of a detection distance that extends in a direction of the surface to be cleaned. The processor may be configured to determine whether an abnormality has been detected based, at least in part, on the measure of the detection distance and may be configured to determine a first velocity estimate based, at least in part, on the detection of the abnormality.