Mobile Robot Server-Based Deep Learning Navigation for New Spaces
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Solution Overview
Problem
Mobile robots based on typical supervised learning techniques struggle to actively receive and reflect actual information required for traveling, leading to difficulties in efficiently navigating new spaces.
Innovation Solution
A mobile robot system that receives information about moving spaces from an external server and performs deep learning to create traveling information, enabling safe and flexible navigation in various environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a mobile robot uses typical supervised learning to receive traveling information from an external server, then the robot can perform basic navigation, but the robot cannot actively receive or reflect actual information required for traveling in new spaces
Solution Approach 1:
The system performs preliminary deep learning processing of moving space information from multiple robots before actual navigation. The server pre-processes and stores traveling information including map data, obstacle locations, and path planning data from multiple robots, making this information readily available when a robot enters a new space, thus resolving the information acquisition delay
Solution Approach 2:
The system creates and utilizes copies of moving space information from multiple robots. Instead of each robot independently exploring and learning new spaces, the server collects, processes, and stores copies of spatial information from multiple robots, allowing any robot to access this pre-processed information and avoid redundant exploration, thereby improving adaptability to new spaces
2Productivity
If a mobile robot relies on supervised learning with pre-input environmental information, then the robot can travel in known environments, but the robot struggles to efficiently navigate new spaces without sufficient information
Solution Approach 1:
The system merges traveling information from multiple robots into a comprehensive database on the server. By combining map data, obstacle information, and path planning data from multiple robots' explorations, the system creates a richer, more complete information set that improves navigation efficiency for all robots entering new spaces
Solution Approach 2:
The system implements a feedback mechanism where robots continuously transmit their sensor data and traveling experiences to the server, which then updates the traveling information database. This feedback loop ensures that the system accumulates and refines information about new spaces over time, improving productivity through increasingly accurate navigation data
Data Source
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AI summary
Disclosed herein are a mobile robot system including a server of creating and storing traveling information about moving space and a mobile robot of travelling on the moving space, wherein the mobile robot comprises a driving portion configured to move the mobile robot, a communication device configured to receive the traveling information from the server and a controller configured to control the driving portion based on the traveling information received from the communication device and wherein the server receives information about the moving space from at least one external robot, and creates the traveling information based on the information about the moving space. Disclosed herein are a mobile robot system and a mobile robot capable of receiving information about moving space received from another mobile robot from an external server, and then performing deep learning based on the information about the moving space so as to travel safely and flexibly in various environments.