Robot Fleet Task Allocation for Tethered and 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 variations in power requirements, with existing approaches failing to account for the distinction between tethered and untethered tasks.
Innovation Solution
A robot fleet management system that allocates robots to tasks based on their power state and the power consumption of each task, distinguishing between tethered and untethered tasks to ensure efficient energy use and task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing fleet management systems allocate robots to tasks without considering power requirements, then task allocation is simplified, but energy efficiency deteriorates
Solution Approach 1:
The system changes the parameter consideration from basic task assignment to include power state parameters. The fleet management system evaluates robot power states and task power consumption parameters to make allocation decisions, transforming the allocation process from simple to parameter-driven optimization
Solution Approach 2:
The system implements feedback by continuously monitoring robot power states and using this information to adjust task allocations. The power state data feeds back into the allocation algorithm, creating a closed-loop system that optimizes energy efficiency based on real-time robot conditions
2Device complexity
If robots are allocated to tasks without distinguishing tethered and untethered tasks, then allocation process is simpler, but task completion reliability deteriorates
Solution Approach 1:
The system segments tasks into distinct categories: tethered tasks and untethered tasks. This segmentation allows the allocation algorithm to apply different criteria for each task type, matching robot capabilities and power states to appropriate task categories, thereby improving task completion reliability
Solution Approach 2:
The system applies local quality by assigning different allocation criteria to different task types. Tethered tasks receive one set of allocation considerations while untethered tasks receive another, optimizing the match between robot characteristics and task requirements for each category
3Speed
If power state is not considered in robot allocation, then allocation speed is faster, but energy optimization deteriorates
Solution Approach 1:
The system performs preliminary action by pre-evaluating robot power states and storing this information before task allocation occurs. This pre-computation of power state data enables the allocation algorithm to make informed decisions without real-time power measurements, maintaining allocation speed while achieving energy optimization
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 may be 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.