Autonomous Machine Task Completion Time Estimation
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Solution Overview
Problem
It is challenging for supervisors to determine the completion time of tasks performed by autonomous machines, such as compactor machines, which affects resource utilization and worksite scheduling.
Innovation Solution
A completion time estimation system that includes a controller configured to obtain parameters associated with a task, determine an estimated completion time, and perform actions based on this estimation, allowing for optimized task initiation and scheduling in autonomous mode.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If an autonomous machine performs compaction tasks without human operator control, then productivity is enhanced and human resources are reduced, but it becomes difficult for supervisors to determine task completion time
Solution Approach 1:
The system implements feedback by continuously monitoring task progress through sensors and GPS tracking, then communicating this information back to the controller which updates the estimated completion time. This closed-loop feedback mechanism ensures supervisors receive real-time information about autonomous machine performance without requiring human operator input.
Solution Approach 2:
The controller acts as an intermediary between the autonomous machine's operational data and the supervisor's information needs. It processes raw sensor data, calculates estimated completion times based on multiple parameters, and presents this processed information to supervisors, bridging the information gap created by autonomous operation.
2Productivity
If task completion time is not accurately determined, then resource utilization and worksite scheduling cannot be optimized
Solution Approach 1:
The system performs preliminary calculations of estimated completion times before tasks are fully executed. By analyzing task parameters, machine performance characteristics, and environmental conditions in advance, the controller provides supervisors with forecasted completion times that enable proactive scheduling and resource allocation decisions.
Solution Approach 2:
The system continuously updates estimated completion times during task execution based on real-time progress feedback. This dynamic adjustment allows supervisors to optimize scheduling decisions as tasks progress, improving both resource utilization and time management through accurate, up-to-date information.
Data Source
AI summary
A machine is disclosed. The machine may include at least one of a propulsion system or a steering system configured to operate under automatic control in an autonomous mode of the machine; and a controller configured to obtain one or more parameters associated with a task that is to be performed in the autonomous mode, determine an estimated completion time for the task based on the one or more parameters associated with the task, and perform one or more actions based on the estimated completion time for the task.