Autonomous Material Spreading With LiDAR Terrain Task Planning
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Solution Overview
Problem
Autonomous control of work vehicles is typically reserved for finishing stages and lacks the capability to independently determine and execute tasks such as leveling and spreading materials across uneven terrain, requiring operator intervention for initial terrain leveling.
Innovation Solution
Autonomous vehicles equipped with stereo vision and LiDAR systems detect objects of interest, generate and update spreading tasks based on real-time data, and navigate to spread materials efficiently, avoiding obstacles and adjusting paths dynamically to achieve desired spreading parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous control is used for finishing stages only, then operator safety is improved, but productivity is worsened due to manual intervention requirements
Solution Approach 1:
The autonomous vehicle system segments terrain preparation into distinct operational phases: initial rough leveling and finishing stages. Each phase can be autonomously executed with appropriate control algorithms, eliminating the need for operator intervention while maintaining safety. The system divides the work area into zones that are progressively refined.
Solution Approach 2:
The system performs preliminary terrain assessment and path planning before autonomous operation begins. Sensors scan the terrain to identify obstacles and determine optimal spreading paths in advance, allowing the vehicle to autonomously execute the entire terrain preparation process including initial leveling without operator involvement.
2Productivity
If autonomous vehicles perform initial terrain leveling, then productivity is improved, but control complexity increases
Solution Approach 1:
The autonomous vehicle system integrates multiple functions into a single platform: terrain sensing, path planning, material spreading, and obstacle avoidance. The vehicle can perform initial rough leveling and finishing stages using the same autonomous control architecture, reducing overall system complexity despite the versatility of operations.
Solution Approach 2:
The system employs continuous feedback loops where sensors monitor terrain conditions, material distribution, and vehicle position in real-time. This feedback is processed by control algorithms that dynamically adjust spreading parameters and navigation paths, enabling complex terrain preparation tasks through relatively simple reactive control rules.
3Manufacturing precision
If real-time terrain scanning is implemented, then spreading precision is improved, but energy consumption increases
Solution Approach 1:
The terrain scanning system operates periodically rather than continuously, scanning the terrain at regular intervals during the spreading process. This periodic operation maintains adequate spreading precision by updating terrain models at sufficient frequencies while significantly reducing energy consumption compared to continuous scanning.
Solution Approach 2:
The system performs comprehensive terrain scanning and creates detailed digital elevation models before the spreading operation begins. This preliminary action allows the vehicle to plan optimal spreading paths and parameters in advance, reducing the need for frequent scanning during operation and thereby lowering energy consumption while maintaining 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 autonomous work vehicles to independently perform tasks like spreading materials across complex terrain, improving efficiency and safety by allowing real-time adjustments and obstacle avoidance, reducing the need for operator intervention in initial terrain preparation.
Implementation Method 1
stereo vision and LiDAR systems detect objects of interest
Implementation Method 2
LiDAR systems detect objects of interest
Implementation Method 3
stereo vision and LiDAR systems detect objects of interest
Data Source
AI summary
A vehicle may be configured to recognize a material pile and locate the material pile within a physical environment. The vehicle may also determine various characteristics or properties associated with the material pile and, based on the determined characteristics, define one or more tasks associated with spreading the material over a defined region according to defined spreading parameters.