Robotic Carpet Drift Estimation Using Multi-Sensor Fusion
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Solution Overview
Problem
Conventional autonomous robots fail to accurately determine their position and control movements on carpeted surfaces due to carpet drift, leading to inaccurate navigation and task execution.
Innovation Solution
The system estimates carpet drift by combining sensor measurements from odometry, gyroscopic, and image sensors to correct odometry data and compensate for carpet grain effects, using a controller to adjust the robot's motion and improve navigation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional autonomous robots use basic sensors for navigation, then device complexity is reduced, but position determination accuracy and navigation precision deteriorate on carpeted surfaces
Solution Approach 1:
The patent combines multiple sensor types (odometry sensors, gyroscopic sensors, and image sensors) into an integrated navigation system. The controller fuses data from all sensors to estimate carpet drift and compensate for it, achieving high position determination accuracy on carpeted surfaces while managing system complexity through coordinated sensor operation.
Solution Approach 2:
The patent introduces image sensors as an intermediary to detect carpet grain direction and drift. These sensors capture visual information about the carpet texture, which the controller uses to estimate and compensate for drift effects, bridging the gap between basic odometry and accurate position determination.
2Manufacturing precision
If the robot uses simple odometry for motion control, then device complexity is low, but navigation accuracy and task execution precision deteriorate due to carpet drift
Solution Approach 1:
The patent implements a feedback mechanism where the controller continuously monitors sensor data, estimates carpet drift based on discrepancies between expected and actual motion, and adjusts the robot's navigation commands accordingly. This closed-loop control compensates for carpet drift and maintains high navigation accuracy.
Solution Approach 2:
The system performs preliminary drift estimation using image sensors to detect carpet grain direction before executing navigation tasks. This advance preparation allows the controller to pre-compensate for expected drift effects, improving navigation accuracy from the outset.
3Reliability
If the robot traverses carpeted surfaces without drift compensation, then device complexity remains low, but position accuracy and movement control precision deteriorate
Solution Approach 1:
The patent makes the navigation system universal by implementing a controller that can operate in both carpeted and non-carpeted environments. The same sensor fusion architecture handles different surface conditions, automatically adapting its drift compensation algorithms based on image sensor detection of carpet grain patterns.
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 approach enhances the robot's ability to accurately navigate and perform tasks on carpeted surfaces by accurately estimating and compensating for carpet drift, ensuring precise positioning and efficient movement.
Implementation Method 1
a second set of sensors including a gyroscope to measure drift of the robot
Implementation Method 2
an image sensor to estimate motion of the robot from changes in reflected light
Data Source
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AI summary
Apparatus and methods for carpet drift estimation are disclosed. In certain implementations, a robotic device includes an actuator system to move the body across a surface. A first set of sensors can sense an actuation characteristic of the actuator system. For example, the first set of sensors can include odometry sensors for sensing wheel rotations of the actuator system. A second set of sensors can sense a motion characteristic of the body. The first set of sensors may be a different type of sensor than the second set of sensors. A controller can estimate carpet drift based at least on the actuation characteristic sensed by the first set of sensors and the motion characteristic sensed by the second set of sensors.