Control method for carpet-induced drift in robot movement, 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 affects their position estimation and motion regularity, leading to inaccurate mapping and inefficient task execution.
Innovation Solution
A control method that utilizes a combination of optical flow sensor data and code disk data to calculate the carpet drift, adjusting the speeds of the robot's drive wheels based on the drift average value to correct the robot's motion direction and maintain linear movement along a preset path.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a robot navigates on a carpeted surface using conventional inertial navigation, then the robot can maintain simple navigation logic, but the robot's position estimation accuracy deteriorates due to carpet drift
Solution Approach 1:
The patent introduces an optical flow sensor as an intermediary device to detect carpet drift. The sensor acts as a mediator between the robot's motion and the carpet's surface characteristics, providing drift detection data that compensates for the inaccuracies in inertial navigation without complicating the overall navigation logic.
Solution Approach 2:
The system implements feedback by continuously monitoring the robot's actual position using the optical flow sensor and comparing it with the expected position from inertial navigation. The detected drift is fed back to the control system, which then compensates for the position estimation errors, maintaining accuracy while preserving simple navigation logic.
2Measurement precision
If an optical flow sensor is used to eliminate carpet influence, then position accuracy is guaranteed, but motion regularity and direction anisotropy elimination cannot be guaranteed
Solution Approach 1:
The patent merges multiple sensing approaches by combining the optical flow sensor with code disk data and inertial navigation. This fusion of multiple data sources compensates for the limitations of each individual sensor, ensuring both position accuracy and motion regularity by cross-validating measurements from different principles.
Solution Approach 2:
The navigation system uses a composite approach by integrating data from heterogeneous sensors (optical flow sensor, code disks, inertial sensors) into a unified navigation solution. This composite sensing strategy leverages the strengths of each sensor type while mitigating their individual weaknesses, ensuring reliable and regular motion control.
3Measurement precision
If fusion calculation of optical flow sensor and code disk data is performed, then navigation accuracy is improved, but computational complexity increases
Solution Approach 1:
The system applies partial fusion by selectively combining data from optical flow sensors and code disks only when needed for carpet drift compensation, rather than continuously fusing all sensor data. This approach improves navigation accuracy in carpeted environments while avoiding unnecessary computational overhead in other situations.
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, improving the robot's navigation accuracy and efficiency by continuously adjusting the drive wheel speeds to align with the intended path, thereby enhancing the robot's ability to perform tasks like vacuuming on carpeted surfaces.
Implementation Method 1
performing, every a first preset time, fusion calculation on data sensed by an optical flow sensor
Implementation Method 2
fusion calculation on data sensed by an optical flow sensor and data sensed by code disks
Data Source
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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, then 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. The control method improves accuracy in detecting carpet drift of the robot, thereby effectively controlling the robot to maintain a good linear walking effect.