Off-Road Route Mapping Using Obstacle Traction Classification
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Solution Overview
Problem
Off-road routing is challenging due to unpredictable obstacles like puddles and wheel tracks, leading to vehicle stalling and costly downtime, especially in environments like quarries where obstacles change frequently and are difficult to map accurately.
Innovation Solution
A computer system that processes sensor data from vehicles to classify obstacles based on size and type, determining required traction capabilities and updating maps to ensure vehicles can navigate safely by removing impassable obstacles from routing plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If vehicles use pre-recorded or computed routes in off-road areas, then routing planning is enabled, but vehicles risk getting stuck in unpredictable obstacles like puddles and wheel tracks
Solution Approach 1:
The system performs preliminary classification and mapping of obstacles by engaging them with work attachments before vehicle routing. Obstacles are categorized by traversability characteristics in advance, allowing vehicles to receive pre-computed routes that account for these classifications, thus preventing getting stuck while maintaining ease of operation
Solution Approach 2:
The system uses feedback from work attachment engagement with obstacles to continuously update the obstacle classification database. This feedback loop improves the accuracy of traversability predictions, enhancing vehicle traversal safety while maintaining efficient routing planning through updated map data
2Ease of operation
If vehicles manually surveil surrounding areas to avoid obstacles, then vehicle operation is maintained, but operator workload increases and response time decreases
Solution Approach 1:
The system enables autonomous vehicles to self-navigate off-road areas by utilizing pre-classified obstacle data from the database. Vehicles independently process routing information without requiring manual surveillance, reducing operator workload while maintaining safe operation through automated decision-making based on classified obstacle characteristics
3Ease of manufacture
If obstacle data is collected without engagement by work attachments, then data collection is simplified, but classification accuracy decreases
Solution Approach 1:
The work attachment serves as an intermediary tool that physically engages with obstacles to collect accurate classification data. Rather than relying on remote sensing alone, the work attachment directly interacts with obstacles to measure traversability characteristics, ensuring high classification accuracy while maintaining a systematic data collection process
4Loss of information
If map data includes all obstacles regardless of vehicle capability, then complete obstacle information is provided, but routing efficiency decreases for vehicles that can traverse certain obstacles
Solution Approach 1:
The system provides customized map data with different levels of obstacle detail tailored to each vehicle's specific capabilities. Vehicles receive filtered obstacle information relevant to their traversability characteristics, allowing them to efficiently navigate by ignoring obstacles they can handle while maintaining awareness of challenging obstacles, thus optimizing routing efficiency without losing critical information
Data Source
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AI summary
A computer system (100) comprising processing circuitry (110) is presented. The processing circuitry (110) is configured to obtain map data (260) for route planning within a confined off-road area (1) and obtain, based on sensor data (13') of a first off-road vehicle (10), position data (212) and size data (214) of a first obstacle (5) within the confined off-road area (1). The processing circuitry (110) is further configured to obtain, based on engagement of the first obstacle (5) by a work attachment (15) of the first off-road vehicle (10), classification data (225) of the first obstacle (5), and determine a first required traction capability (231) for traversing the first obstacle (5) based on the size data (214) and the classification data (225). The processing circuitry (110) is further configured to update the map data (260) with the position data (212), size data (214) and the first required traction capability (231) for traversing the first obstacle (5).