Robot Battery Charging and Task Redistribution Under Peak Demand
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Solution Overview
Problem
Conventional robot control systems inefficiently manage robot batteries by issuing immediate charging commands when the state of charge drops below a certain threshold, regardless of the robot's remaining tasks or overall work distribution, leading to suboptimal operation and resource utilization.
Innovation Solution
A method and apparatus that manage robot batteries based on operation data, considering real-time battery state, work peak times, and task distribution, by collecting and monitoring data to determine optimal charging and task redistribution among robots, including immediate charging, post-task charging, and charging at the nearest station.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If immediate charging commands are issued when battery level drops below threshold, then battery reliability is maintained, but robot productivity decreases due to unplanned task interruptions
Solution Approach 1:
The system performs preliminary charging actions before the battery level drops to critical thresholds. By monitoring battery levels and proactively scheduling charging during low-demand periods or redistributing tasks in advance, the system prevents unplanned interruptions while maintaining productivity. This resolves the contradiction by acting beforehand rather than reactively when thresholds are breached.
Solution Approach 2:
The system dynamically adjusts charging schedules and task distributions based on real-time battery levels, task priorities, and operational demands. Instead of fixed threshold-based charging, the system flexibly optimizes when and how robots charge, balancing reliability maintenance with productivity preservation through adaptive decision-making.
2Reliability
If robots are charged during peak usage times, then battery availability is improved, but system productivity decreases due to reduced operational robots
Solution Approach 1:
The system implements periodic charging schedules that align with usage patterns, charging robots during low-demand periods rather than peak times. This periodic approach ensures batteries are recharged regularly without removing operational robots during critical periods, thus maintaining both availability and productivity.
Solution Approach 2:
The system temporarily removes robots from active service during non-peak periods for charging (discarding them from current tasks), then recovers them for continued operation. This cyclical discarding and recovering ensures battery availability is maintained while minimizing impact on overall system productivity.
3Productivity
If task distribution is optimized for current battery states, then short-term productivity is improved, but long-term battery reliability deteriorates due to insufficient charging
Solution Approach 1:
The system continuously monitors battery levels and uses this feedback to adjust both task distribution and charging schedules. By incorporating real-time battery state feedback into decision-making, the system balances short-term productivity needs with long-term reliability requirements, ensuring robots are charged before critical thresholds are reached while maintaining optimal task allocation.
Solution Approach 2:
The system performs preliminary task redistribution and charging scheduling based on projected battery depletion trends rather than waiting for critical levels. This proactive approach allows optimization of both productivity and reliability by planning ahead based on current battery states and expected consumption patterns.
Data Source
AI summary
An apparatus for managing a robot battery based on operation data may include a robot data collecting unit configured to periodically collect data related to an operation of a robot, a charged state monitoring unit configured to periodically monitor a charged state of the battery of the robot based on the collected data, a charging management unit configured to issue a battery charge command to the robot based on the data and the charged state, and a task distribution command unit configured to issue a distribution command with respect to a remaining task to the robot based on the data and the charged state.