Teleoperator Microtask Assignment Using Skill-Based Workflow Queues
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Solution Overview
Problem
Existing teleoperation systems face inefficiencies in utilizing teleoperators with varying skill levels and geographical distribution, leading to equipment downtime and reduced productivity due to unoptimized task assignment.
Innovation Solution
A control center generates quality control profiles for teleoperators, evaluates optimization criteria, and assigns microtasks based on skill levels and workflow sequences, utilizing machine learning to optimize task distribution across a pool of teleoperators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If teleoperators are assigned to equipment based on simple availability rather than skill matching, then task assignment is faster and simpler, but task completion quality decreases and equipment downtime increases
Solution Approach 1:
The system changes the parameters of task assignment by incorporating skill level matching, quality control profiles, and workflow optimization criteria. Instead of simple availability-based assignment, the system evaluates multiple parameters including teleoperator expertise, equipment requirements, and workflow sequences to optimize both productivity and quality simultaneously
Solution Approach 2:
The system performs preliminary actions by pre-establishing quality control profiles for teleoperators and pre-defining workflow sequences before actual task assignment occurs. This preparation enables faster real-time decision-making while maintaining high-quality matching between teleoperators and equipment
2Reliability
If equipment is held offline for inspections or blocked work, then quality control is maintained, but teleoperator utilization decreases and idle time increases
Solution Approach 1:
The system implements dynamic task assignment that adapts in real-time to equipment status changes. When equipment is offline for inspections or blocked, the system dynamically reassigns teleoperators to available equipment or alternative tasks, maintaining quality control through the same profiling system while minimizing idle time through flexible, real-time adjustments
Solution Approach 2:
The system ensures continuity of useful action by maintaining a pool of teleoperators who can be rapidly reallocated when equipment becomes unavailable. Through workflow sequences and quality control profiles, the system ensures that teleoperators continuously engage in valuable work across different equipment rather than experiencing idle periods
3Productivity
If microtasks are assigned without considering workflow sequences, then task assignment is faster, but overall workflow completion time increases and coordination becomes difficult
Solution Approach 1:
The system segments workflows into discrete microtasks while maintaining workflow sequence integrity. Each microtask is assigned to the most suitable teleoperator based on quality control profiles, enabling parallel processing of independent microtasks while preserving overall workflow coordination and completion speed
Data Source
AI summary
A method and system may generate a quality control profile to indicate an expertise level and one or more skills of a teleoperator(s). The control center evaluates optimization criteria for a workflow to assign performance of microtasks of the workflow to select teleoperators from a pool of teleoperators. Each teleoperator accesses teleoperation functionality for remote control of a plurality of types of equipment at one or more defined geographic areas and each teleoperator is remotely located from the defined geographic areas. The control center generates queues for each of the select teleoperators that include corresponding assigned microtasks.