Rough-Terrain Vehicle Control Using Mapped Surface Unevenness
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Solution Overview
Problem
Rough terrain vehicles face challenges in adapting their control requirements to varying road surface conditions, leading to inefficient travel and potential vehicle instability.
Innovation Solution
A control requirement determiner that uses past data associating location information with behavior information to determine the necessary control requirements for a rough terrain vehicle's control targets, such as torque generators and suspensions, during travel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the vehicle travels on bad roads with uneven surfaces, then the vehicle can operate in rough terrain environments, but the vehicle body experiences excessive vibration and instability
Solution Approach 1:
The system performs preliminary detection of road surface conditions using sensors before the vehicle encounters them, and pre-calculates appropriate control requirements for suspension and torque generators. This allows the vehicle to adapt to rough terrain while maintaining stability by having control parameters ready in advance based on detected road conditions.
Solution Approach 2:
The system continuously monitors vehicle body behavior (acceleration, attitude) and road surface conditions through sensors, feeds this information back to the control requirement determiner, which then adjusts control parameters for suspension and torque generators in real-time to maintain vehicle stability on rough terrain.
2Productivity
If the control requirements are fixed and not adapted to road conditions, then the control system is simple, but the vehicle travels inefficiently and experiences instability
Solution Approach 1:
The control system dynamically adjusts control requirements for suspension and torque generators based on real-time detection of road surface conditions and vehicle behavior. Instead of fixed control parameters, the system continuously modifies control characteristics to match current operating conditions, improving travel efficiency while managing complexity through systematic adaptation.
Solution Approach 2:
The system changes control parameters (such as torque values, suspension stiffness, damping coefficients) based on detected road surface conditions and vehicle behavior. By dynamically adjusting these parameters rather than using fixed values, the vehicle achieves efficient travel across varying terrain while the complexity is managed through structured parameter adaptation rules.
3Reliability
If real-time sensors are added to detect road surface conditions, then the vehicle can adapt control requirements, but the device complexity increases
Solution Approach 1:
The system uses multi-functional sensors that detect both vehicle body behavior (acceleration, attitude) and road surface conditions with a single integrated sensing approach. This universal detection method improves control accuracy by providing comprehensive information while avoiding the complexity of separate specialized sensor systems for each function.
Data Source
AI summary
A control requirement determiner includes: a storage that stores at least one piece of location information and one piece of behavior information as past data in which the location information and the behavior information are associated with each other, the location information indicating a geographic location, the behavior information being related to a behavior exhibited by a vehicle body of a vehicle at the location indicated by the location information when the vehicle traveled in the past; and processing circuitry that calculates, based on the vehicle speed information, the acceleration information, and the sprung weight information of the past data, a degree of unevenness of a road surface, and determines, based on the calculated degree of unevenness of the road surface, a control requirement to be imposed on a control target of a rough terrain vehicle at the location indicated by the location information of the past data during travel.