Autonomous Earth-Moving Vehicle Control for Obstacle Navigation
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Solution Overview
Problem
Existing autonomous control systems for earth-moving construction and mining vehicles face limitations in using limited types of sensed data, inability to perform fully autonomous operations when encountering on-site obstacles, and the need for bulky and expensive hardware systems.
Innovation Solution
The implementation of an Earth-Moving Vehicle Autonomous Movement Control (EMVAMC) system that integrates data from various sensors such as GPS, LiDAR, and infrared sensors to determine and control the movement of vehicles around obstacles, allowing for autonomous navigation and coordination 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 the ability to perform fully autonomous operations when faced with on-site obstacles is compromised
Solution Approach 1:
The patent combines multiple sensor types (cameras, LiDAR, radar, ultrasonic sensors, GPS) into a unified autonomous control system that processes data from all sources simultaneously. This integration enables the system to achieve full autonomous operation capability by merging the strengths of different sensing technologies to detect and respond to various on-site obstacles effectively.
Solution Approach 2:
The autonomous control system is designed with multi-functional capability to handle diverse obstacle types (static obstacles, dynamic obstacles, terrain variations) using a single integrated platform. The system can perform multiple functions including obstacle detection, path planning, vehicle control, and coordination with other vehicles, eliminating the need for separate specialized systems for each function.
2Extent of automation
If multiple sensor types are integrated for full autonomous operation, then autonomous operation capability is improved, but device complexity increases
Solution Approach 1:
The autonomous control system processes sensor data in segmented stages: perception (detecting obstacles and environment), interpretation (analyzing sensor data to understand scene), decision-making (determining appropriate actions), and execution (controlling vehicle movements). This segmentation of the control process manages complexity by breaking down the autonomous operation into manageable functional modules.
Solution Approach 2:
The patent introduces a central processing unit or controller that acts as an intermediary between multiple sensor inputs and the vehicle's actuation systems. This intermediary component integrates and coordinates data from cameras, LiDAR, radar, and other sensors, translating diverse sensor signals into coherent control commands, thereby managing system complexity through centralized coordination.
3Reliability
If traditional hardware systems are used for autonomous control, then reliability is maintained, but cost and size increase
Solution Approach 1:
The patent replaces traditional mechanical control systems with electronic and software-based autonomous control systems. Instead of relying on bulky mechanical sensors and dedicated hardware controllers, the system uses computer vision algorithms, machine learning models, and integrated electronic sensor arrays that process information digitally, reducing physical size while maintaining or improving reliability through advanced computational methods.
Solution Approach 2:
The system changes the operational parameters of the control architecture by transitioning from deterministic mechanical control to adaptive software-based control. This allows the system to adjust its behavior dynamically based on real-time sensor data and environmental conditions, improving reliability through flexibility while reducing hardware requirements through efficient algorithmic processing.
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, including movement around and removal of obstacles, with improved precision and efficiency, reducing the need for expensive hardware and enhancing operational capabilities.
Implementation Method 1
a plurality of GPS receivers mounted at a plurality of respective positions on a chassis of a body of the powered earth-moving vehicle to receive GPS signals
Implementation Method 2
a LiDAR component configured to obtain LiDAR data and to detect distance and shape of an obstacle located between the current position and the target destination location
Implementation Method 3
a real-time kinematic (RTK) radio mounted on the powered earth-moving vehicle to receive RTK-based GPS correction data from a remote base station
Data Source
AI summary
Systems and techniques are described for implementing autonomous control of powered earth-moving vehicles, including to automatically determine and control movement around a site having potential obstacles. For example, the systems/techniques may determine and implement autonomous operations of powered earth-moving vehicle(s) (e.g., obtain/integrate data from sensors of multiple types on a powered earth-moving vehicle, and use it to determine and control movement of the powered earth-moving vehicle around a site), including in some situations to implement coordinated actions of multiple powered earth-moving vehicles and/or of a powered earth-moving vehicle with one or more other types of construction vehicles. The described techniques may further include determining current location and positioning of the powered earth-moving vehicle on the site, determining a target destination location and/or path of the powered earth-moving vehicle, identifying and classifying obstacles (if any) along a desired path or otherwise between current and destination locations, and implementing actions to address any such obstacles.