UGV Relative Mapping for INS Drift and Obstacle Navigation
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Solution Overview
Problem
Unmanned ground vehicles (UGVs) face challenges in navigating through areas with obstacles due to inaccuracies in GPS and INS data, which can lead to collisions or unsafe proximity to obstacles, especially in dense environments where GPS is unreliable or unavailable.
Innovation Solution
The UGV generates a relative map of its surroundings using onboard scanning devices and updates its position using INS data, classifying cells as traversable or non-traversable based on scanning output, with cell sizes selected to account for INS drift, allowing for accurate obstacle avoidance and navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If GPS-based navigation is used to track UGV location, then navigation coverage is extended, but measurement precision deteriorates due to inaccuracies and jitter
Solution Approach 1:
The patent introduces a map as an intermediary reference framework that is continuously updated with obstacle information from scanning devices. Instead of relying solely on GPS coordinates, the system tracks the UGV's position relative to this dynamic map, using the map as a mediator to translate GPS data into accurate relative positioning information that accounts for environmental features and obstacles.
Solution Approach 2:
The patent replaces direct reliance on GPS mechanical positioning with a computational approach using scanning devices (LIDAR, cameras) to create and update environmental maps. This substitution transforms the navigation problem from absolute coordinate tracking to relative position estimation based on environmental features, thereby improving precision while maintaining broad coverage.
2Adaptability or versatility
If INS is used to track UGV position, then navigation works without GPS, but measurement precision deteriorates due to accumulated drift
Solution Approach 1:
The patent implements continuous feedback by repeatedly scanning the environment with scanning devices and updating the map with newly detected obstacles and features. This feedback loop allows the system to correct INS drift by comparing expected positions (based on INS) with actual observed positions (based on map features), thereby maintaining accuracy over extended periods without GPS.
Solution Approach 2:
The system performs preliminary mapping actions by continuously scanning and building the environmental map before navigation decisions are made. This preliminary action of creating a detailed reference framework enables more accurate position determination when INS data is processed against the pre-established map, reducing the impact of drift.
3Measurement precision
If cell size in map is reduced to improve navigation accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent applies local quality by using smaller cell sizes only in regions where the UGV is currently located or where high precision is needed for obstacle avoidance, while using larger cell sizes in distant or less critical areas of the map. This selective approach maintains high navigation accuracy where needed while reducing overall map data complexity and processing requirements.
Data Source
AI summary
A system and method of navigating a vehicle, the vehicle comprising a scanning device and a self-contained navigation system (SCNS) operatively connected to a computer, the method comprising: operating the scanning device for repeatedly executing a scanning operation, each operation includes scanning an area surrounding the vehicle, thereby generating respective scanning output data; operating the computer for generating, based on the scanning output data, a relative map representing at least a part of the area, the map having known dimensions and being relative to a position of the vehicle, wherein the map comprises cells, each cell classified to a class from at least two classes, comprising traversable and non-traversable, and characterized by dimensions equal or larger than an accumulated drift value of the SCNS; wherein non-traversable cells correspond to identified obstacles; receiving SCNS data and updating a position of the vehicle relative to the cells based on the SCNS data.