Autonomous Machine Navigation for Energy-Aware Return Charging
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Solution Overview
Problem
Existing autonomous machines waste energy and battery life by randomly searching for a boundary wire to find their base station for charging.
Innovation Solution
An autonomous machine equipped with a navigation system that determines remaining battery energy, calculates an efficient path to a destination, and estimates travel energy threshold to optimize energy usage and minimize downtime for charging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the autonomous machine randomly searches for the boundary wire to find the base station, then the machine can locate the base station, but energy and battery life are wasted
Solution Approach 1:
The navigation system pre-calculates the most energy-efficient path to the base station before the machine actually travels, using current position and battery status to determine the optimal route in advance, avoiding random searching behavior
Solution Approach 2:
The system continuously monitors battery energy levels and adjusts navigation decisions based on real-time feedback, comparing remaining energy against estimated travel requirements to dynamically optimize the path to base station
2Loss of time
If the autonomous machine follows the boundary wire to reach the base station, then the machine can navigate to the destination, but time is lost due to inefficient path selection
Solution Approach 1:
The navigation system pre-calculates the most time-efficient path to the base station before the machine actually travels, using current position and operational context to determine the optimal route in advance
Solution Approach 2:
The path planning is dynamic and adapts to changing conditions, allowing the machine to adjust its route to base station based on real-time factors such as battery status and work region constraints
3Reliability
If the autonomous machine does not accurately predict battery energy needs, then the machine can continue operating, but the machine may run out of energy and cannot return to base station
Solution Approach 1:
The navigation system implements continuous feedback loops that monitor actual energy consumption versus predicted consumption, using this data to refine and improve the accuracy of future energy predictions while maintaining manageable system complexity
Solution Approach 2:
The system uses its own operational data and sensor information to self-calibrate and improve energy prediction accuracy over time, reducing the need for external calibration or complex pre-programming
Data Source
AI summary
An autonomous machine may be returned to a base station for charging based on remaining battery energy and an estimated travel energy threshold. The estimated travel energy threshold may be determined based on a direct and obstacle-free route from the machine's current position to the base station and an estimated energy consumed per unit distance, which may be updated. The remaining battery energy may be calculated using a battery management system.