Autonomous Surface Vehicle Route Adjustment for Low-Friction Surfaces
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Solution Overview
Problem
Autonomous surface vehicles face challenges in maintaining route integrity due to changing surface friction coefficients, which can lead to slipping or loss of control, increasing the risk of accidents.
Innovation Solution
A computing device and method that determine the frictional coefficient of surfaces along the predefined route of an autonomous surface vehicle and modify the route in real-time to avoid slippery surfaces, utilizing machine learning models to construct stylized images and estimate friction coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous surface vehicle follows a predefined route without modification, then the route planning is simple and efficient, but the vehicle may slip or lose control when encountering surfaces with low friction coefficients
Solution Approach 1:
The system performs preliminary friction coefficient determination for surfaces along the predefined route before the vehicle reaches them. By assessing surface conditions in advance and identifying low-friction areas ahead of time, the system can proactively modify the route to avoid slippery surfaces, thereby preventing slippage and maintaining vehicle control stability without requiring complex real-time reactions
Solution Approach 2:
The system continuously determines friction coefficients of surfaces along the route and uses this feedback information to dynamically modify the vehicle's path. The friction coefficient data serves as feedback that informs route adjustment decisions, allowing the system to adapt to changing surface conditions and maintain reliable vehicle control while navigating
2Reliability
If the system determines friction coefficients for all surfaces along the route, then the vehicle can avoid slippery surfaces, but the computational time and processing complexity increase
Solution Approach 1:
Instead of uniformly analyzing all surfaces along the entire route, the system applies friction coefficient determination selectively to specific local areas that the vehicle is likely to encounter. By focusing computational resources on relevant surface segments rather than the entire route, the system maintains route safety through targeted friction assessment while minimizing unnecessary computational time and processing overhead
Data Source
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AI summary
A computing device, method and computer program product are provided for facilitating modification of the route of an autonomous surface vehicle. In the context of a method, a stylized image of an input image, such as a photographic image, is constructed. The stylized image distinguishes between surfaces having different texture that are present in the input image. Based upon the stylized image, the method also includes determining a frictional coefficient associated with at least some of the surfaces having different texture. The method further includes determining whether the route of the autonomous surface vehicle is to be altered from a predefined route based upon the frictional coefficient that has been determined to be associated with a surface over which the predefined route of the autonomous surface vehicle extends.