Robot Carpet Drift Estimation Using Odometry and Visual Sensing
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Solution Overview
Problem
Conventional autonomous robots fail to accurately determine their position and pose on carpeted surfaces due to carpet drift, leading to navigation errors and inefficient task execution.
Innovation Solution
A robotic system equipped with a combination of sensors, including odometry sensors, gyroscopic sensors, and image sensors, which estimate carpet drift by comparing actuation and motion characteristics, allowing the controller to adjust movements and correct odometry data to maintain accurate navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional autonomous robots use standard odometry sensors to determine position and pose, then the robot can navigate on hard surfaces, but the robot experiences navigation errors and position estimation inaccuracies when moving on carpeted surfaces due to carpet drift
Solution Approach 1:
The patent combines multiple sensor types (odometry sensors, gyroscopic sensors, and image sensors) into an integrated sensing system. The controller processes data from all sensors simultaneously to estimate carpet drift by comparing actuation characteristics with actual motion characteristics, thereby resolving the contradiction between maintaining navigation reliability and achieving measurement precision on carpeted surfaces
Solution Approach 2:
The system implements feedback by continuously comparing the robot's commanded motion (from actuator sensors) with its actual motion (from gyroscopic and image sensors). The controller uses this feedback to detect and estimate carpet drift, then compensates for it in real-time, improving both navigation reliability and position estimation accuracy on carpeted surfaces
2Measurement precision
If the robot uses multiple types of sensors to detect and compensate for carpet drift, then navigation accuracy on carpeted surfaces improves, but the device complexity increases
Solution Approach 1:
The controller serves multiple functions: it processes odometry data for basic navigation, integrates gyroscopic sensor data for orientation tracking, incorporates image sensor data for visual odometry, and performs carpet drift estimation and compensation. This multi-functionality reduces the need for separate dedicated systems for each function, thereby managing device complexity while achieving high measurement precision
Solution Approach 2:
The controller acts as an intermediary that integrates and harmonizes data from diverse sensor types. By using a unified control algorithm that processes all sensor inputs together, the system avoids the complexity of multiple separate processing systems while achieving accurate carpet drift estimation through the synergistic use of all 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
The system effectively compensates for carpet drift, improving the robot's ability to navigate and perform tasks on carpeted surfaces by providing precise position and pose estimation, enhancing efficiency and safety.
Implementation Method 1
The second set of sensors may include a gyroscopic sensor configured to sense rotation of the body
Implementation Method 2
The second set of sensors includes an image sensor configured to capture two or more images. The controller is configured to estimate the motion characteristic by comparing the two or more images
Data Source
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.