Robot Charging Station Placement Using Movement Density Analysis
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Solution Overview
Problem
Existing methods for determining the location of charging stations for robots often result in inefficient movement paths due to a lack of understanding of the workspace and working attributes of the robots, leading to inefficient resource usage, especially in larger spaces with multiple robots.
Innovation Solution
A method that analyzes movement path information to determine the density of regions and recommends a location for the charging station based on this analysis, using a combination of movement path profiles and driving information to identify candidate regions with high density and low congestion, and employs an artificial neural network for optimizing the location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the charging station location is determined subjectively by a manager, then the implementation is simple, but the movement efficiency of robots deteriorates
Solution Approach 1:
The system enables robots to autonomously determine optimal charging station locations by analyzing their own movement path data. The robot collects movement information, processes it through density calculation algorithms, and independently identifies optimal charging locations without requiring manual manager intervention, thus improving movement efficiency while maintaining operational simplicity.
Solution Approach 2:
The patent replaces the manual subjective decision-making process with an automated computational system. Instead of managers visually assessing and deciding charging station locations, the system uses algorithms that process movement path data, calculate region densities, and automatically determine optimal locations, substituting mechanical human judgment with computational analysis.
2Loss of time
If the charging station is placed in a location not understood by the manager, then the placement process is quick, but the resource usage efficiency deteriorates
Solution Approach 1:
The system performs preliminary analysis of movement paths and workspace characteristics before determining charging station locations. By pre-processing movement data, calculating region densities in advance, and identifying optimal locations before actual deployment, the system eliminates the need for time-consuming trial-and-error placements while ensuring energy-efficient locations are selected from the outset.
Solution Approach 2:
The system continuously collects movement path information from robots and uses this feedback to refine charging station location recommendations. By monitoring actual robot movements and charging patterns, the system adjusts its density calculations and location suggestions to optimize resource usage efficiency over time, preventing energy waste from poor initial placements.
3Reliability
If the robot moves to a charging station in an inefficient location, then the charging function is maintained, but the movement time increases
Solution Approach 1:
The system changes the parameter used for location selection from arbitrary or manual coordinates to density-based metrics derived from actual movement paths. By calculating region densities based on movement frequency and proximity to task areas, the system identifies locations that minimize travel time while ensuring charging functionality, directly addressing the time loss issue without compromising charging reliability.
4Quantity of substance
If the workspace is large with multiple robots, then the operational capacity increases, but the inefficiency of movement paths amplifies resource loss
Solution Approach 1:
The system segments the large workspace into multiple regions and calculates density metrics for each region independently. This segmentation allows the system to identify optimal charging locations for different areas of the workspace, enabling multiple robots to access nearby charging stations rather than all converging on a single distant location, thereby reducing cumulative travel time and energy loss across the entire robot fleet.
Data Source
AI summary
A method, an apparatus, and a system for recommending a location of a charging station of a robot are disclosed. The method includes obtaining movement path information from one or more robots in a space including a plurality of regions, determining density of each of the plurality of regions based on the obtained movement path information, and determining a recommended location of a charging station for charging the one or more robots from the plurality of regions based on the determined density. In a 5G environment connected for the Internet of things, the method for recommending a location of a charging station is implemented by executing an artificial intelligence algorithm or machine learning algorithm.