Onboard Point Cloud Mapping for Container Vehicle Path Planning
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Solution Overview
Problem
The construction of a planning map for autonomous mobile machines in cargo container transportation vehicles is challenging due to varying docking locations and poses, necessitating high positioning accuracy and complex external sensor installations with maintenance costs.
Innovation Solution
An autonomous mobile machine equipped with a sensor generates a point cloud map during movement, projecting and processing the data to create a planning map for path planning within the vehicle, reducing the need for external sensors and their associated complexities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external sensors are installed to achieve high positioning accuracy, then positioning accuracy is improved, but device complexity and maintenance costs increase
Solution Approach 1:
The autonomous mobile machine uses its own onboard sensor to scan and construct maps of the cargo container transportation vehicle, eliminating the need for external sensors. The machine serves itself by performing mapping and positioning functions independently, reducing system complexity and maintenance requirements while maintaining high positioning accuracy through self-contained navigation capabilities
Solution Approach 2:
Instead of using external sensors to directly measure position, the system creates a digital copy (point cloud map) of the physical environment. This virtual map is then used for positioning and path planning, replacing the need for complex external sensing infrastructure while achieving accurate location determination through map matching and spatial recognition
2Measurement precision
If external sensors are installed to achieve high positioning accuracy, then positioning accuracy is improved, but maintenance costs increase
Solution Approach 1:
The autonomous mobile machine performs its own mapping and positioning using onboard sensors, eliminating dependency on external sensing equipment that would require maintenance. The self-contained system reduces maintenance costs by removing external sensor installations while maintaining positioning accuracy through continuous environmental scanning and map construction
Solution Approach 2:
The system creates and maintains a digital copy of the environment rather than relying on external physical sensors. This virtual mapping approach transfers the sensing function to the mobile machine itself, eliminating external hardware that would incur maintenance costs while preserving measurement precision through software-based position determination
3Area of stationary object
If the autonomous mobile machine moves to multiple locations for mapping, then map coverage is improved, but loss of time increases
Solution Approach 1:
The autonomous mobile machine continuously scans and collects point cloud data during its movement between locations, rather than stopping to perform discrete mapping operations. This continuous data collection approach ensures complete map coverage while minimizing mapping time, as the machine performs useful work (transportation) simultaneously with map construction
Solution Approach 2:
The system performs preliminary map construction by collecting point cloud data during the machine's necessary movements for task execution. By integrating mapping with transportation operations, the system prepares the environmental model in advance without requiring separate dedicated mapping time, achieving both comprehensive coverage and operational efficiency
Data Source
AI summary
An autonomous mobile machine includes a controller. The controller is configured to execute program instructions to implement following steps: controlling, in response to a mapping task instruction, the autonomous mobile machine to move from a mapping start point to a mapping end point, the mapping end point being located in a loading space of a cargo container transportation vehicle; generating a point cloud map during a process of the movement by using point cloud data scanned by the sensor, the point cloud data including point cloud data obtained by scanning the cargo container transportation vehicle; and obtaining a planning map based on the point cloud map. The planning map is used to plan a path within the cargo container transportation vehicle.