Autonomous Machine Scheduling for Variable Availability Windows
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Solution Overview
Problem
Existing autonomous grounds maintenance machines lack efficient scheduling mechanisms that adapt to changing conditions without requiring user input, leading to suboptimal maintenance of work regions.
Innovation Solution
The development of an autonomous machine system that determines an operating schedule by identifying windows of availability, assigning operational tasks based on total operation time, and adjusting for user input, environmental factors, and priority tasks, ensuring effective maintenance of work regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous machines operate without scheduling mechanisms, then operational simplicity is maintained, but maintenance quality deteriorates
Solution Approach 1:
The scheduling system dynamically adjusts operational parameters based on real-time conditions. The controller continuously monitors environmental factors, machine status, and task progress, then adapts the schedule accordingly. This dynamic approach ensures high maintenance quality while keeping the scheduling mechanism flexible rather than rigidly complex.
Solution Approach 2:
The autonomous machine performs self-scheduling through its onboard controller that automatically generates and adjusts operational schedules without external intervention. The system monitors its own status and environmental conditions, then autonomously determines optimal maintenance timing and task allocation, eliminating the need for complex external scheduling infrastructure.
2Adaptability or versatility
If fixed schedules are used for autonomous operation, then operational predictability is improved, but adaptability to changing conditions deteriorates
Solution Approach 1:
The scheduling controller continuously receives feedback from sensors monitoring environmental conditions, machine status, and task progress. This real-time feedback loop enables the system to detect changes in conditions and automatically adjust the operational schedule, achieving high adaptability without significant time loss as adjustments occur continuously rather than periodically.
Solution Approach 2:
The system performs preliminary scheduling based on predicted conditions, then makes incremental adjustments as actual conditions diverge from predictions. This approach maintains operational predictability while adapting to changes, as the base schedule is already established but can be modified through continuous feedback without requiring complete re-scheduling.
3Manufacturing precision
If comprehensive task monitoring is implemented, then maintenance precision is improved, but system complexity deteriorates
Solution Approach 1:
The scheduling controller acts as an intermediary that receives data from multiple sensors and processes it to determine task execution precision. Rather than requiring direct complex connections between all sensors and control functions, the controller mediates information flow, consolidating data and making decisions based on aggregated inputs, thereby achieving high precision with manageable system complexity.
Data Source
AI summary
A method for operating an autonomous machine involves determining one or more windows of availability over a time period when the autonomous machine is allowed to operate to perform one or more operational tasks. An operating schedule is determined for a work region that assigns the one or more operational tasks to the one or more windows of availability based a total operation time. At least one operational task is prioritized to cover an isolated containment zone before at least one other operational task to cover an autonomous containment zone in the operating schedule.