Material Handling Vehicle Position Estimation Under Sensor Noise
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Solution Overview
Problem
Existing systems face challenges in accurately tracking the location of material handling vehicles within a warehouse due to sensor noise and environmental factors, leading to difficulties in integrating with warehouse management systems and ensuring safe operation.
Innovation Solution
A method and system that combines odometry data from a first sensor with inertial measurement unit data from a second sensor using a Kalman filter to generate an updated location estimate, allowing for tracking objects outside the vehicle's field of view and adjusting operational parameters to prevent collisions, while integrating with warehouse management systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is used to track vehicle location, then location tracking capability is provided, but sensor noise reduces measurement accuracy
Solution Approach 1:
The patent combines data from multiple sensors (odometry sensor, inertial measurement unit, visual sensor) to track vehicle location. By merging multiple data sources, the system compensates for individual sensor noise and improves overall measurement accuracy and reliability of location tracking.
Solution Approach 2:
The patent introduces a Kalman filter as an intermediary processing layer between raw sensor data and location estimation. The Kalman filter acts as a mediator that processes noisy sensor inputs, reduces measurement errors, and produces reliable location estimates by optimally combining predictions from odometry with measurements from the inertial measurement unit.
2Measurement precision
If multiple sensors are combined to improve accuracy, then location estimation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent employs a Kalman filter that serves multiple functions: it processes odometry data, integrates inertial measurement unit data, reduces noise from multiple sensors, and generates location estimates. This multi-functional approach consolidates complex processing requirements into a single algorithmic framework, managing system complexity while maintaining high measurement precision.
3Loss of information
If visual sensor field of view is limited, then sensor design is simplified, but objects outside field of view cannot be tracked
Solution Approach 1:
The patent uses feedback from the Kalman filter's continuous location estimation to track objects that move in and out of the visual sensor's field of view. The system maintains object tracking information based on vehicle location updates, allowing it to reconstruct the positions of objects even when they are not currently visible, thus reducing information loss without requiring multiple overlapping camera fields of view.
Data Source
AI summary
Systems and methods of tracking a material handling vehicle include estimating a location of the material handling vehicle based on modeled location data from a first sensor configured to model an odometry of the vehicle, measuring the location of the material handling vehicle based on measured location data from a second sensor configured to measure a relative motion of the vehicle, determining an amount of noise in both the modeled location data from the first sensor and the measured location data from the second sensor, and updating the estimated location of the material handling vehicle based on the location data from the first sensor, the second sensor, and the amount of noise.