Autonomous Vehicle Trajectory Feedback for Real-Time Limiting Factor Detection
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Solution Overview
Problem
Existing autonomous vehicle control systems for mine sites struggle to identify and quantify factors causing productivity losses in real-time, such as obstacles and traction issues, leading to inefficiencies and delays.
Innovation Solution
A method and system for managing autonomous vehicle operations that continuously compare the actual travel trajectory with the expected trajectory, identify incident limiting factors causing deviations, and adjust the expected trajectory accordingly to optimize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional post-processing techniques are used to analyze LHD performance at the end of each cycle, then data analysis is simplified, but productivity loss factors cannot be identified in real-time
Solution Approach 1:
The system performs preliminary data collection and analysis during the operation cycle itself, rather than waiting for post-processing. Sensors continuously monitor trajectory, speed, and operational parameters, and the controller identifies incident limiting factors as they occur, enabling real-time detection of productivity loss factors without waiting for cycle completion
Solution Approach 2:
The system implements continuous feedback by comparing actual trajectory data with expected trajectory data in real-time. The controller receives ongoing inputs from sensors, processes this information during operation, and identifies incident limiting factors immediately when deviations are detected, providing timely feedback rather than delayed post-cycle analysis
2Productivity
If real-time trajectory monitoring and comparison is implemented, then incident limiting factors can be identified immediately, but system complexity increases
Solution Approach 1:
The controller serves multiple functions: it manages autonomous operation, collects sensor data, determines expected trajectories, compares actual with expected trajectories, and identifies incident limiting factors. By making the controller multi-functional, the system achieves real-time monitoring capabilities without adding separate dedicated hardware for each function, thus managing complexity while maintaining productivity benefits
3Productivity
If the expected trajectory is adjusted dynamically based on incident limiting factors, then optimal performance is achieved, but control system complexity increases
Solution Approach 1:
The expected trajectory is made dynamic rather than static. The controller continuously adjusts the expected trajectory based on real-time identification of incident limiting factors, allowing the trajectory to adapt to changing conditions such as obstacles, terrain variations, or operational constraints. This dynamic adjustment enables optimal performance while the controller integrates the adjustment logic into its existing autonomous operation management functions
Data Source
AI summary
A method or system for managing autonomous vehicle operations includes receiving inputs including a task command for an autonomous mobile machine, determining an expected travel trajectory for the autonomous mobile machine to perform the task command, and determining an actual travel trajectory of the mobile machine during performance of the task command. The method or system further includes comparing the actual travel trajectory to the expected travel trajectory, and identifying an incident limiting factor responsible for a deviation between the actual travel trajectory and the expected travel, wherein comparing the actual travel trajectory is repeated at predetermined intervals during performance of the task command.