Mobile Robot Self-Mapping for Arrival Location and Route Planning
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Solution Overview
Problem
Existing serving robots require manual generation of 2D maps and route setting, which is time-consuming and necessitates specialized training, hindering their deployment in new spaces without additional effort.
Innovation Solution
A method and robot using LiDAR sensors and cameras to automatically generate a 2D map, recognize objects, and set an arrival location by determining obstacle-free paths, optimizing the route based on data-driven algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual map generation and route setting are used, then the robot can operate with simple equipment, but the time and effort required increases significantly
Solution Approach 1:
The robot performs self-mapping and self-localization by autonomously capturing images, generating 2D maps, and determining its position without human intervention. The system automatically processes images to create floor plans and identifies traversable spaces, eliminating the need for manual map creation while enabling deployment in new environments quickly
Solution Approach 2:
The patent replaces manual mechanical map drawing and route marking with automated image processing and algorithmic route optimization. Computer vision algorithms process captured images to generate 2D maps, and optimization algorithms automatically determine routes, substituting human manual work with automated computational systems
2Device complexity
If manual map generation is required, then the system structure remains simple, but specialized training is needed for operation
Solution Approach 1:
The robot autonomously performs map generation and route planning without requiring user intervention or specialized knowledge. The system automatically captures images, processes them to create 2D maps, determines traversable spaces, and optimizes routes, making the robot operable by users without specialized training
Solution Approach 2:
The patent implements a universal system that combines image capture, 2D map generation, object recognition, and route optimization into a single integrated platform. This multi-functional approach allows the robot to adapt to various environments and tasks without requiring specialized procedures or training for different operations
3Productivity
If automated map generation is implemented, then deployment time is reduced, but the device complexity increases
Solution Approach 1:
The patent divides the complex automated mapping system into distinct functional modules: image capture module, 2D map generation module, object recognition module, and route optimization module. Each module performs a specific function and can be processed independently, making the overall complex system manageable and implementable through modular components
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
Enables efficient and automated setting of arrival locations and route optimization, reducing the need for manual intervention and specialized training, thus facilitating easier deployment in new environments.
Implementation Method 1
generating a 2D LiDAR map using LiDAR sensors
Data Source
AI summary
A method for automatic arrival location on a map and data-based route optimization, includes: generating, by one or more processors of a robot, a 2D LiDAR map using LiDAR sensors and cameras implemented on the robot, wherein the 2D LiDAR map includes information related to objects comprising a table or a chair; setting, by the one or more processors, an arrival location of the robot from the 2D LiDAR map.