Control method for carpet drift in robot motion, chip, and cleaning robot
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Solution Overview
Problem
Conventional autonomous robots face challenges in accurately navigating carpeted environments due to carpet drift, which causes position estimate errors and inefficient motion, as they are affected by friction forces and direction-dependent carpet fibers, leading to deviations from intended paths.
Innovation Solution
A control method that utilizes a combination of optical flow sensors and code disks to calculate carpet drift by converting sensed data into a global coordinate system, adjusting drive wheel speeds based on drift statistics to correct the robot's motion direction, ensuring accurate navigation and map building.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a robot navigates on a carpeted surface using conventional inertial navigation, then the robot can move autonomously, but the robot's position estimation becomes inaccurate due to carpet drift
Solution Approach 1:
The patent implements feedback control by continuously monitoring the robot's actual position through optical flow sensors and code disks, comparing it with the expected position from inertial navigation, and generating correction signals to compensate for carpet drift. The system uses the detected drift information to adjust wheel speeds dynamically, creating a closed-loop control system that eliminates position estimation errors caused by carpet effects.
Solution Approach 2:
The patent introduces optical flow sensors and code disks as intermediary measurement devices that directly measure the robot's motion relative to the carpet surface. These sensors act as mediators between the robot's motion and the control system, providing accurate position and speed information that is not affected by carpet drift, thereby enabling precise compensation of the drift through calculated correction values.
2Measurement precision
If the robot uses optical flow sensor to eliminate carpet influence, then position accuracy is improved, but motion regularity and direction accuracy cannot be guaranteed
Solution Approach 1:
The patent merges multiple sensing systems (optical flow sensors, code disks, and inertial navigation) into a unified control system. By combining the position accuracy from optical flow sensors with the motion control capabilities of code disks and the global positioning from inertial navigation, the system achieves both accurate position measurement and regular motion control, eliminating the limitations of using any single sensor type alone.
Solution Approach 2:
The patent dynamically changes control parameters (wheel speeds, steering angles) based on real-time drift detection and correction calculations. The system adjusts motion parameters continuously to maintain both position accuracy and motion regularity, transforming the rigid control approach into an adaptive control strategy that responds to carpet conditions while ensuring smooth and regular robot motion.
3Reliability
If the robot continuously adjusts drive wheel speeds to compensate for carpet drift, then navigation accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent implements self-service control where the robot's own motion data, collected through its sensors, is used to generate correction signals for compensating carpet drift. The system uses its built-in sensors (optical flow sensors, code disks, inertial navigation) to detect drift and automatically calculates and applies corrections without external intervention, reducing the need for additional complex external correction devices while maintaining high navigation accuracy.
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 method effectively compensates for carpet drift by continuously adjusting drive wheel speeds, allowing the robot to maintain a straight path and improve navigation accuracy and efficiency on carpeted surfaces.
Implementation Method 1
performing, every a first preset time, fusion calculation on data sensed by an optical flow sensor and data sensed by code disks at the same time
Implementation Method 2
performing, every a first preset time, fusion calculation on data sensed by an optical flow sensor and data sensed by code disks at the same time, to obtain a current position coordinate of the robot, which corresponds to an actual advancing distance of drive wheels of the robot
Implementation Method 3
the motion of the robot is not only pushed by a friction force, but also affected by an acting force applied to the robot from the carpet
Data Source
AI summary
A control method for carpet drift in robot motion, a chip, and a cleaning robot are disclosed. The control method includes: performing fusion calculation on a current position coordinate of the robot according to data sensed by a sensor every first preset time, calculating amount of drift, relative to a preset direction, of the robot, according to a relative position relationship between a current position and an initial position of the robot, and accumulating to obtain a drift statistical value; and calculating the number of acquisitions of the position coordinate within a second preset time, averaging to obtain a drift average value, determining a state of the robot deviating from the preset direction according to the drift average value, and setting a corresponding Proportion Integration Differentiation (PID) proportionality coefficient to synchronously adjust speeds of left and right drive wheels of the robot while reducing a deviation angle of the robot.