Autonomous Machine Scheduling for Changing Work Region Conditions
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Solution Overview
Problem
Existing autonomous grounds maintenance machines lack efficient scheduling mechanisms that adapt to changing conditions without user input, making it difficult to maintain work regions effectively.
Innovation Solution
The development of autonomous machine functionality that determines an operating schedule for autonomous grounds maintenance machines, allowing them to adapt to changing conditions and maintain work regions without requiring user input, by utilizing sensors, navigation systems, and scheduling controllers to optimize maintenance tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous machines operate without scheduling mechanisms, then operational flexibility is maintained, but maintenance effectiveness deteriorates
Solution Approach 1:
The autonomous machine performs self-scheduling by automatically determining when and where to execute maintenance tasks based on sensor data and operational conditions, eliminating the need for external user scheduling while maintaining effective maintenance through autonomous decision-making
Solution Approach 2:
The scheduling mechanism dynamically adapts to changing operational conditions by continuously monitoring sensor data and adjusting maintenance schedules in real-time, allowing the system to respond to varying work region conditions without requiring complex pre-planned schedules
2Extent of automation
If autonomous machines require user input for scheduling, then scheduling accuracy improves, but operational autonomy deteriorates
Solution Approach 1:
The system autonomously generates and executes maintenance schedules without requiring user input by using its own sensor data and operational information to determine optimal maintenance timing and locations, fully实现ing self-service scheduling
Solution Approach 2:
The scheduling system continuously receives feedback from sensors monitoring work region conditions and machine status, using this feedback to automatically adjust and optimize maintenance schedules without external intervention, closing the control loop for autonomous operation
3Reliability
If autonomous machines adapt to changing conditions, then maintenance quality improves, but system complexity increases
Solution Approach 1:
The system dynamically adapts to changing work region conditions by continuously monitoring sensor data and adjusting maintenance schedules in real-time, enabling high-quality maintenance through flexible response to environmental changes without requiring overly complex adaptation mechanisms
Solution Approach 2:
The scheduling system adapts to changing conditions by modifying operational parameters such as maintenance timing, frequency, and location based on sensor feedback, allowing quality maintenance through parameter adjustment rather than complex system restructuring
4Productivity
If autonomous machines optimize operational schedules, then productivity improves, but computational requirements increase
Solution Approach 1:
The scheduling optimization is segmented into discrete decision points based on sensor triggers and operational milestones, allowing the system to optimize maintenance schedules through incremental computational steps rather than requiring intensive continuous processing, improving productivity with manageable computational loads
Data Source
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AI summary
This disclosure provides an autonomous machine system having a scheduling controller configured to determine 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; determine a total operation time over the time period; and determine an operating schedule for a work region that assigns one or more operational tasks to the one or more windows of availability based on the total operation time. The autonomous machine may be commanded to operate in the work region according to the operating schedule.