Autonomous Robot 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 method involves estimating carpet drift using a combination of odometry, gyroscopic, and image sensors to calculate the carpet drift vector, which is then used to correct odometry data and improve navigation by compensating for the effects of carpet grain direction on robot motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional autonomous robots use standard navigation methods, then they can operate on hard surfaces, but they fail to accurately determine position and control movements on carpeted surfaces due to carpet drift
Solution Approach 1:
The system dynamically adapts its navigation approach based on the detected surface type. When carpet is detected, the robot switches from standard odometry-based navigation to a corrected navigation mode that compensates for carpet drift, allowing accurate operation on both hard surfaces and carpeted surfaces
Solution Approach 2:
The system changes the navigation parameters and control algorithms based on the surface type detected. On carpeted surfaces, it applies drift compensation parameters and adjusted motion control to maintain positioning accuracy, while using standard parameters on hard surfaces
2Measurement precision
If robots use multiple sensors to estimate carpet drift, then navigation accuracy on carpet improves, but device complexity increases
Solution Approach 1:
The navigation system is segmented into distinct modules: a surface detection module that identifies carpet vs. hard surfaces, a drift estimation module that calculates carpet drift vectors, and a correction module that applies compensation. This modular segmentation allows the system to use complex multi-sensor drift estimation only when needed on carpeted surfaces, keeping the overall system manageable
Solution Approach 2:
The system uses its existing sensors (odometry, gyroscopic, and image sensors) for their primary functions while also utilizing them for carpet drift estimation. The image sensors used for navigation and obstacle detection also serve to detect carpet surfaces, and the odometry/gyro data used for basic navigation also provide the foundation for drift calculation, eliminating the need for separate dedicated drift sensors
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 enables autonomous robots to accurately navigate and perform tasks on carpeted surfaces by accurately estimating and compensating for carpet drift, enhancing their positioning and motion control.
Implementation Method 1
a second plurality of measurements indicative of a change in a rotation of the body of the robotic device, wherein the second plurality of measurements is obtained using a gyroscopic sensor
Implementation Method 2
a third plurality of measurements indicative of a change in a path angle of the robotic device, wherein the third plurality of measurements is obtained using an image sensor
Data Source
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AI summary
Methods for carpet drift estimation of a robotic device include sensing an actuation characteristic of the actuator system, for example, odometry sensors for sensing wheel rotations of the actuator system. An image sensor senses a motion characteristic of the body. A controller estimates carpet drift based at least on the actuation characteristic and the actual motion sensed by the image sensor.