Autonomous Earth-Moving Vehicle Sensor Architecture for Obstacle Navigation
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Solution Overview
Problem
Existing autonomous earth-moving vehicles face limitations in performing fully autonomous operations due to reliance on limited sensed data, inability to navigate on-site obstacles, and the need for bulky and expensive hardware systems for data processing, particularly for camera image analysis.
Innovation Solution
A hardware component architecture for autonomous control of earth-moving vehicles, incorporating multiple types of sensors and a vehicle control unit that processes data to enable fully autonomous operations, including positional, location, and track alignment sensors, allowing for obstacle detection and coordinated actions between vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If camera images are used for autonomous operations, then visual data can be captured, but bulky and expensive hardware systems with specialized cooling systems and complex processing units are required
Solution Approach 1:
The patent segments the data collection function by using multiple types of sensors (cameras, LIDAR, GPS, inertial sensors) that can operate independently and contribute different types of data. This segmentation allows the system to capture visual and spatial information without requiring a single complex processing unit, thereby reducing overall hardware complexity while maintaining comprehensive data capture capabilities.
Solution Approach 2:
The control system is designed to process multiple types of sensor data (camera images, LIDAR point clouds, GPS coordinates, inertial measurements) through a unified control algorithm. This multi-functional approach allows the same processing unit to handle diverse data types without requiring specialized hardware for each sensor type, reducing system complexity and cost.
2Speed
If camera images are processed at sufficient speeds, then autonomous operations can be performed, but specialized cooling systems and complex processing units are required
Solution Approach 1:
The patent divides the processing workload by having each sensor type process its own data stream independently through dedicated algorithms (e.g., LIDAR processes point clouds, cameras process images, GPS provides positioning). This segmentation allows parallel processing of multiple data types at reduced computational complexity for each individual stream, achieving sufficient overall processing speed without requiring a single complex high-speed processing unit with specialized cooling.
Solution Approach 2:
The system processes sensor data at the level of detail required for autonomous operation without attempting to process every possible detail. For example, LIDAR data is processed to identify obstacles and terrain features sufficient for navigation, rather than creating complete high-resolution 3D models of every surface. This partial processing approach achieves adequate speed without the computational overhead that would require complex cooling systems.
3Device complexity
If limited types of sensed data are used, then hardware systems can be simpler, but fully autonomous operations cannot be performed when faced with on-site obstacles
Solution Approach 1:
The patent merges data from multiple sensor types (camera images for visual identification, LIDAR for 3D spatial mapping, GPS for positioning, inertial sensors for motion detection) into a unified environmental model. This combination allows the system to detect and respond to various types of obstacles (rocks, terrain changes, equipment) using complementary sensor data, achieving full autonomous obstacle handling capability while keeping individual sensor components relatively simple and cost-effective.
Solution Approach 2:
The control system acts as an intermediary that integrates and reconciles data from multiple simple sensor types. Rather than using a single complex sensor, the system combines outputs from multiple simpler sensors (each detecting different aspects of the environment) to create a comprehensive understanding of obstacles and terrain, enabling versatile autonomous operation without requiring any single complex hardware component.
Data Source
AI summary
Systems and techniques are described for implementing autonomous control of powered earth-moving vehicles (e.g., construction and/or mining vehicles), including to automatically determine and control movement around a site. For example, the systems/techniques may determine and implement autonomous operations of earth-moving vehicles by determining current location and positioning of an earth-moving vehicle on the site, determining a command for the earth-moving vehicle, and causing the earth-moving vehicle to perform the command—the autonomous operations may in some situations further include obtaining and integrating data from sensors of multiple types on the earth-moving vehicle, implementing coordinated actions of multiple earth-moving vehicles of one or more types, etc.