Autonomous Machine Noise-Aware Control for Worksite Productivity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Noise restrictions at worksites, which vary by time of day, day of the week, and year, limit the operation of machines, leading to decreased productivity as machines may need to be stopped during heightened noise restriction periods.
Innovation Solution
A system and method that use time-specific maximum noise limit data to control autonomous machines, switching between noise-optimized modes to maintain productivity while adhering to noise limits, by adjusting machine operation parameters such as engine power, transmission settings, and movement patterns to ensure sound output does not exceed allowed levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machines operate at maximum productivity levels, then productivity is improved, but noise level increases and may exceed maximum noise limit levels
Solution Approach 1:
The system dynamically adjusts machine operation parameters based on real-time noise monitoring and predicted future noise levels. The autonomous machine modifies its operational characteristics (speed, power output, task execution rate) to maintain productivity while ensuring noise levels remain within predicted maximum limits for upcoming time periods.
Solution Approach 2:
The system changes operational parameters of the autonomous machine based on predicted noise levels. By adjusting parameters such as engine power, transmission settings, and movement patterns, the machine can operate at optimized productivity levels while keeping noise output within acceptable limits for different times of day.
2Object-generated harmful factors
If machines are stopped during heightened noise restriction time periods, then noise level is reduced to comply with restrictions, but productivity decreases
Solution Approach 1:
The system performs preliminary noise level predictions for upcoming time periods and proactively adjusts machine operation parameters before noise restrictions become active. This allows the machine to continue operating during transition periods and maintain productivity while ensuring compliance with future noise limits.
Solution Approach 2:
The system enables continuous operation of autonomous machines by dynamically adjusting operational parameters rather than stopping work. The machine maintains continuous productive action while adapting its noise output to match predicted maximum noise limits across different time periods, eliminating idle time associated with traditional shutdown approaches.
3Object-generated harmful factors
If different noise-optimized modes are used for different time periods, then noise compliance is improved, but machine operation complexity increases
Solution Approach 1:
The autonomous machine performs self-adjustment of operational parameters based on predicted noise levels and active restrictions. The machine autonomously selects and implements appropriate noise-optimized modes without requiring external intervention or complex manual reconfiguration, simplifying operation while maintaining compliance.
Solution Approach 2:
The system uses real-time noise level monitoring and prediction feedback to automatically adjust machine operation parameters. This closed-loop control enables the machine to adapt to different noise restrictions seamlessly, managing operational complexity through automated feedback-driven adjustments rather than manual mode switching.
Data Source
AI summary
A system, method, and apparatus can provide control signaling to control one or more autonomous machines during a plurality of predefined periods of time. Each of the one or more autonomous machines can be controlled according to a maximized productivity level for each task performed by the autonomous machine while at the same time generating sound during performance of the task at the maximized productivity level no louder than respective maximum noise limit levels specific for the plurality of predefined periods of time.