Autonomous Device Path Planning Using Energy Probability Grids
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Solution Overview
Problem
Current surface processing devices, such as autonomous vacuum cleaners and lawn mowers, do not effectively consider battery usage when selecting routes, leading to inefficient power consumption and battery depletion.
Innovation Solution
A method that divides the operating area into a grid, assigns probability factors to each cell based on energy consumption, and updates these factors using a reference grid to optimize the path and reduce power usage, allowing for more efficient battery management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the device focuses on surface coverage and completes cleaning tasks, then productivity is improved, but energy consumption increases and battery life decreases
Solution Approach 1:
The system performs preliminary actions by mapping the operating area and dividing it into a grid structure before actual cleaning begins. Energy consumption probabilities are pre-calculated for each cell based on surface characteristics, allowing the device to plan energy-efficient paths in advance rather than reacting to energy depletion during operation.
Solution Approach 2:
The system continuously updates energy consumption probabilities for each grid cell based on real-time measurements of actual energy consumption during operation. This feedback mechanism allows the device to learn from past performance and optimize future path selection to minimize battery usage while maintaining productivity.
2Duration of action of moving object
If the device runs until battery is low to maximize operational time, then duration of action is improved, but reliability decreases due to risk of incomplete surface processing
Solution Approach 1:
The system calculates energy consumption probabilities for all grid cells before operation begins, creating a predictive model of battery usage. This allows the device to determine the optimal stopping point before battery depletion occurs, ensuring complete surface processing while maximizing operational duration without risking incomplete tasks.
3Use of energy by moving object
If the device returns to base station for recharging frequently to manage battery usage, then energy efficiency is improved, but productivity decreases due to interruptions
Solution Approach 1:
The system pre-calculates energy consumption for each grid cell and determines the optimal number of cleaning passes required for each area. This allows the device to maximize continuous operation time between base station returns, minimizing interruptions while maintaining energy efficiency through informed path selection based on pre-computed energy probabilities.
Data Source
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AI summary
A method for determining an energy-efficient path of an autonomous device wherein said autonomous device moves over a global grid of cells into which a given operating area has been split, the method being characterized in that determination of said energy-efficient path comprises the steps of: processing of the current cell (201); taking a measurement or of the processing (202); classifying the measurement σ to be of a particular level Σ (203), taking into account a predefined division, of the measurements results range, into a plurality of measurements levels; storing said classified measurement in a memory of the autonomous device (204) and associating it with the current cell; selecting a reference probability grid (205); updating (207) the probabilities by applying the reference grid (100) to the global grid at its current position such that every cell on the reference grid (100) corresponds unambiguously to one cell on the global grid; and moving the autonomous device to a next cell of the global grid (208) and setting said next cell as the current cell (201) in order to process the next cell.