On-Vehicle LiDAR Mapping for Autonomous Earth-Moving Control
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Solution Overview
Problem
Existing techniques for autonomous control of powered earth-moving vehicles are limited by the use of limited types of sensed data, inability to perform fully autonomous operations when faced with on-site obstacles, and the requirement for bulky and expensive hardware systems, which hinder coordinated operations between multiple vehicles.
Innovation Solution
The implementation of on-vehicle perception systems using LiDAR components and additional sensors to gather and analyze data, enabling the creation of 3D maps and obstacle classification, and the use of an Earth-Moving Vehicle Autonomous Operations Control (EMVAOC) system to control vehicle movements and coordinate actions between multiple vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If limited types of sensed data are used for autonomous control, then device complexity is reduced, but measurement precision and reliability of autonomous operations deteriorate
Solution Approach 1:
The patent combines multiple types of sensors (LiDAR, cameras, radar, ultrasonic sensors) into an integrated perception system that gathers diverse environmental data simultaneously. This merging of different sensing modalities enables comprehensive 3D mapping and obstacle detection without requiring overly complex individual sensor systems, resolving the contradiction between device complexity and measurement precision.
Solution Approach 2:
The perception system is designed to perform multiple functions including 3D mapping, obstacle detection, classification, and tracking using a unified architecture. This multi-functional approach allows the system to gather comprehensive environmental data while maintaining manageable device complexity through shared processing resources and integrated hardware platforms.
2Device complexity
If limited types of sensed data are used, then device complexity is reduced, but the ability to perform fully autonomous operations when faced with on-site obstacles deteriorates
Solution Approach 1:
The patent combines multiple types of sensors (LiDAR, cameras, radar, ultrasonic sensors) into an integrated perception system that gathers diverse environmental data simultaneously. This merging of different sensing modalities enables comprehensive 3D mapping and obstacle detection without requiring overly complex individual sensor systems, resolving the contradiction between device complexity and measurement precision.
Solution Approach 2:
The system continuously processes sensor data to generate real-time feedback about the environment, including obstacle detection, classification, and tracking. This feedback loop enables the autonomous vehicle to adapt its operations dynamically, improving reliability by continuously monitoring and responding to on-site conditions while maintaining manageable hardware complexity through efficient processing algorithms.
3Measurement precision
If bulky and expensive hardware systems are used to support autonomous operations, then measurement precision and reliability improve, but ease of operation and coordination between multiple vehicles deteriorate
Solution Approach 1:
The perception system is designed to perform multiple functions including 3D mapping, obstacle detection, classification, and tracking using a unified architecture. This multi-functional approach allows the system to gather comprehensive environmental data while maintaining manageable device complexity through shared processing resources and integrated hardware platforms.
Solution Approach 2:
The patent implements a modular perception system with distinct sensor modules (LiDAR, cameras, radar, ultrasonic sensors) that can be independently optimized and replaced. This segmentation allows each sensor type to be selected based on specific performance requirements rather than requiring all vehicles to use overly complex unified systems, improving ease of operation and coordination while maintaining measurement precision.
4Measurement precision
If comprehensive sensors are used to gather multiple types of data, then measurement precision and autonomous operation capability improve, but device complexity increases
Solution Approach 1:
The patent combines multiple types of sensors (LiDAR, cameras, radar, ultrasonic sensors) into an integrated perception system that gathers diverse environmental data simultaneously. This merging of different sensing modalities enables comprehensive 3D mapping and obstacle detection without requiring overly complex individual sensor systems, resolving the contradiction between device complexity and measurement precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables fully autonomous operations of earth-moving vehicles, improves data accuracy and processing efficiency, reduces hardware requirements, and allows for coordinated actions between multiple vehicles, enhancing operational precision and safety.
Implementation Method 1
In at least some embodiments, a perception system on a powered earth-moving vehicle includes one or more LiDAR components (e.g., light emitters and light detector/receiver sensors) that repeatedly (e.g., continuously) map an environment around the vehicle
Data Source
AI summary
Systems and techniques are described for implementing autonomous control of earth-moving construction and/or mining vehicles, including to automatically determine and control autonomous movement (e.g., of a vehicle's hydraulic arm(s), tool attachment(s), tracks/wheels, rotatable chassis, etc.) to move materials or perform other actions based at least in part on data about an environment around the vehicle(s). A perception system on a vehicle that includes at least a LiDAR component may be used to repeatedly map a surrounding environment and determine a 3D point cloud with 3D data points reflecting the surrounding ground and nearby objects, with the LiDAR component mounted on a component part of the vehicle that is moved independently of the vehicle chassis to gather additional data about the environment. GPS data from receivers on the vehicle may further be used to calculate absolute locations of the 3D data points.