Robot Navigation Bias Calibration for Accurate Route Recreation
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Solution Overview
Problem
Robots face challenges in accurately recreating learned routes due to biases and noise in sensor and odometry data, leading to navigation errors and reduced performance.
Innovation Solution
A system and method that utilize a processor to navigate a robot, collect data from sensors, generate a probability distribution function (PDF) of parameters, determine parameter values, and produce a map, accounting for biases in sensors and odometry units to enhance navigation and localization capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If robot uses sensor and odometry data for route recreation, then navigation capability is enabled, but measurement biases cause navigation accuracy to deteriorate
Solution Approach 1:
The system implements feedback by comparing the robot's actual position during route execution with the expected position from stored odometry data. This feedback loop enables real-time detection of biases in sensor measurements, allowing the system to identify and correct navigation drift caused by encoder and sensor inaccuracies.
Solution Approach 2:
The system performs preliminary calibration by collecting and analyzing sensor data during a training phase before actual route execution. During this preliminary action, the robot learns its inherent measurement biases and compensates for them in advance, improving navigation accuracy before the biased sensors would cause errors during operational route recreation.
2Measurement precision
If robot collects and processes sensor data to determine parameter biases, then navigation accuracy is improved, but computational complexity increases
Solution Approach 1:
The system extracts and isolates bias parameters from the complex sensor data stream by separating the systematic error components from the random noise. This extraction process focuses computational resources on identifying and correcting the most significant sources of inaccuracy, reducing the overall computational complexity while maintaining high measurement precision.
Solution Approach 2:
The system transforms raw sensor measurements into corrected parameters by applying bias compensation factors. This parameter change approach converts inaccurate odometry and sensor readings into accurate navigation parameters through mathematical transformations, improving measurement precision without requiring fundamentally more complex processing architecture.
3Productivity
If robot executes trained route using stored odometry data, then route recreation is achieved, but sensor biases cause position accuracy to deteriorate
Solution Approach 1:
During route execution, the system continuously monitors actual sensor measurements against expected values from the trained route. This real-time feedback enables detection of position drift caused by accumulated sensor biases, allowing the robot to correct its position estimates and maintain accurate navigation despite using stored odometry data.
Solution Approach 2:
The system performs self-calibration by using its own sensor measurements during route execution to identify and correct its positioning errors. The robot serves itself by detecting biases in its own odometry system and compensating for them without requiring external intervention, maintaining position accuracy while efficiently executing the trained route.
Data Source
AI summary
Systems, apparatuses, and methods for bias determination and value calculation of parameters of a robot are disclosed herein. According to at least one exemplary embodiment, a bias in a navigation parameter may be determined based on a bias in one or more measurement units, wherein a navigation parameter may be a parameter useful to a robot to recreate a route such as, for example, velocity and the bias may be accounted for to more accurately recreate the route and generate accurate maps of an environment.