Autonomous Ground Vehicle Fault Detection via Velocity Twist Comparison
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Solution Overview
Problem
Autonomous Ground Vehicles (AGVs) face inefficiencies and potential damage due to mechanical faults like drive shaft or wheel joint issues, which can cause erratic movement and lead to erroneous local path planning, consuming more power and potentially worsening if not detected timely.
Innovation Solution
A method and system using an AI model to detect mechanical faults by comparing actual and optimal velocity twists of the AGV along trajectory plan segments, based on displacement parameters and vehicle weight, with a fault detection device equipped with processors and memory to execute this comparison in real-time navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the control unit applies extra torque to compensate for mechanical faults, then the AGV maintains its trajectory, but power consumption increases and fuel efficiency deteriorates
Solution Approach 1:
The system performs preliminary detection of mechanical faults by comparing actual velocity twist with optimal velocity twist before significant deviations occur. By detecting drive shaft or wheel joint issues early through velocity parameter analysis, the system can address problems proactively rather than applying continuous extra torque, thereby reducing power consumption while maintaining trajectory accuracy.
2Productivity
If mechanical faults are not detected timely, then the AGV continues operation, but the damage worsens and leads to erroneous path planning
Solution Approach 1:
The system implements continuous feedback monitoring by comparing actual velocity twist parameters with optimal values during real-time operation. When deviations exceed thresholds, the system generates fault alerts, enabling timely detection of mechanical issues while the AGV is still operational. This feedback mechanism allows proactive maintenance scheduling without interrupting overall productivity.
3Reliability
If the system implements real-time velocity twist comparison for fault detection, then mechanical faults are detected timely, but system complexity increases
Solution Approach 1:
The system uses the AGV's existing velocity sensors and control unit to perform self-diagnosis by comparing actual velocity twist with pre-calculated optimal values. This self-service approach leverages already-present hardware and software resources, avoiding the need for additional specialized detection equipment and minimizing system complexity while achieving reliable fault detection.
Data Source
AI summary
This disclosure relates to method and system for detecting and compensating for mechanical fault in autonomous ground vehicle (AGV). For each of a set of trajectory plan segments along a base path during real-time navigation of the AGV, the method may include receiving a plurality of vehicle displacement parameters along a given trajectory plan segment. and determining an optimal velocity twist of the AGV in the given trajectory plan segment using an artificial intelligence (AI) model, based on the plurality of vehicle displacement parameters and a weight of the AGV. The method may further include determining the mechanical fault in the AGV based on a comparison of an actual velocity twist of the AGV in the given trajectory plan segment and the optimal velocity twist of the AGV in the given trajectory plan segment for each of the set of trajectory plan segments.