Robot Fleet Power-Aware Task Allocation for Untethered Work
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Solution Overview
Problem
Existing fleet management systems for mobile robots are inefficient in allocating robots to tasks due to varying power requirements, particularly when tasks are both tethered and untethered, leading to suboptimal resource utilization and scheduling challenges.
Innovation Solution
A robot fleet management system that considers the power state and consumption of each robot and task to efficiently allocate robots to either tethered or untethered tasks based on their capabilities, using onboard power sources and external power stations for replenishment during tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If robots are allocated to untethered tasks without considering power state, then task allocation is simplified, but power depletion and task interruption occur
Solution Approach 1:
The system performs preliminary assessment of robot power states before allocating untethered tasks. The fleet management system evaluates current battery levels and predicts whether robots have sufficient power to complete intended tasks, preventing power depletion issues before they occur.
Solution Approach 2:
The system continuously monitors robot power states and uses this feedback to dynamically adjust task allocations. Power state information flows back to the fleet management system, which recalculates optimal assignments based on current battery levels and task requirements.
2Reliability
If robots are frequently recharged during operations, then power state is maintained, but productivity decreases due to interruptions
Solution Approach 1:
The system proactively identifies robots that need recharging before their power is fully depleted. By predicting power consumption based on current task assignments and battery levels, the system schedules recharging during natural task transitions or low-demand periods, minimizing operational disruptions.
Solution Approach 2:
The fleet management system dynamically adjusts task allocations in real-time based on changing power states. When robots require recharging, the system redistributes their tasks to other available robots, ensuring continuous productivity while maintaining power state thresholds.
3Speed
If power consumption is not considered in task allocation, then allocation speed is faster, but resource utilization becomes suboptimal
Solution Approach 1:
The system pre-calculates and stores power consumption profiles for different task types and robot configurations. This preliminary data preparation enables rapid comparison and decision-making during actual allocation, maintaining high speed while ensuring optimal resource utilization through informed choices.
Solution Approach 2:
The system uses power consumption parameters as key decision variables in the allocation algorithm. By incorporating energy efficiency metrics into the optimization function, the system balances allocation speed with resource utilization efficiency, selecting tasks that maximize overall fleet productivity.
Data Source
AI summary
In an implementation of a method of operation of a robot fleet management system, the robot fleet management system accesses a set of tasks available to be performed by a fleet of robots, accesses a respective power consumption for each task from the set of tasks, and accesses a respective power state of each robot in the fleet. The robot fleet management system allocates a selected robot to a selected task, based at least in part on the power state of at least the selected robot and the power consumption for at least the selected task. The power consumption is determined by the robot fleet management system and/or be provided by the task provider. The set of tasks includes tethered and untethered tasks. The robot fleet management system allocates the selected robot to an untethered task after determining the selected robot has sufficient power to complete the untethered task.