Prescription Dispensing Robot SLAM Mapping for Wheel-Slip Correction
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Solution Overview
Problem
Current robotic mapping technologies in pharmacies are inaccurate due to wheel slips and skidding, leading to potential errors in prescription dispensing, and there is a need for a more precise location service to automate pharmaceutical and medical sectors.
Innovation Solution
Implementing Simultaneous Localization and Mapping (SLAM) using rotary encoders and laser scans to create accurate maps, correcting for wheel slips and skidding, thereby enhancing the precision of robotic navigation and dispensing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If dead reckoning is used for robotic navigation in pharmacies, then the system is simple to implement, but the mapping accuracy deteriorates due to wheel slips and skidding
Solution Approach 1:
The patent introduces laser range finders as an intermediary sensing mechanism to compensate for the inaccuracies of dead reckoning. The laser range finders provide external reference measurements that mediate between the robot's self-motion model and the actual environment, correcting position estimates without requiring complex hardware modifications to the robot itself.
Solution Approach 2:
The system implements feedback by continuously comparing the position estimates from dead reckoning with actual measurements from laser range finders. This feedback loop allows the robot to correct its navigation errors caused by wheel slips and skidding, improving mapping accuracy while maintaining system simplicity.
2Device complexity
If dead reckoning is used for robotic navigation, then the system requires fewer sensors, but the location tracking accuracy deteriorates
Solution Approach 1:
Laser range finders serve as an intermediary measurement system that provides external validation of the robot's position. These sensors mediate between the robot's internal position estimates and the actual environment, enabling accurate location tracking without requiring complex sensor arrays or advanced mechanical systems.
Solution Approach 2:
The patent replaces reliance on mechanical wheel encoders with optical laser ranging for position verification. This substitution eliminates the fundamental limitation of mechanical systems (wheel slips and skidding) by using non-contact optical measurements that are not affected by surface conditions or mechanical imperfections.
3Ease of manufacture
If wheel encoders are used to track robot position, then the system is cost-effective, but the location accuracy deteriorates due to wheel slips
Solution Approach 1:
The patent uses laser range finders as an intermediary verification system that checks and corrects position measurements from wheel encoders. This intermediary layer provides a cost-effective solution by using relatively inexpensive laser sensors to compensate for the inherent inaccuracies of mechanical encoder systems without requiring expensive alternative positioning hardware.
Solution Approach 2:
The system implements feedback correction where laser range finder measurements are used to adjust and correct position estimates from wheel encoders. This feedback mechanism maintains cost-effectiveness by continuing to use simple mechanical encoders while improving accuracy through periodic correction from laser measurements.
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
SLAM provides a more accurate mapping solution, reducing errors in prescription dispensing and improving the efficiency and safety of pharmacy automation systems.
Implementation Method 1
dead reckoning as a mapping method in navigation of robots, which calculates the current position of the robot by using a previously determined position
Implementation Method 2
Simultaneous localization and mapping (SLAM) is a technique used by robots and autonomous vehicles to build up a map within an unknown environment
Data Source
AI summary
A pharmacy automation system having a robot having a hardware device and a software for internal mapping to perform simultaneous localization and mapping (SLAM) is disclosed herein. The robot is configured to use the SLAM technique to carry out at least the following different interactions: the robot communicates autonomously with a physician or an assistant directly or via an intermediary; the robot interacts with an inventory of goods and browses the inventory of goods to determine if a prescribed medication is available in the pharmacy; if the prescribed medication is available in the pharmacy, the robot interacts with a medication dispenser, using the internal mapping to fill a container with the prescribed medication, and store the container; when a patient or a proxy arrives to pick up the prescribed medication, the robot checks and approves an identification of the patient or the proxy; and when the patient or proxy presents a prescription containing the prescribed medication, the robot retrieves the container with the prescribed medication and hands the container with the prescribed medication over to the patient or proxy.