Palletizing method for unknown incoming materials, control device and computer-readable storage medium
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Solution Overview
Problem
Conventional robotic arms are limited to handling fixed-size boxes, leading to unstable placement and inefficient palletizing when dealing with inconsistent box sizes in logistics.
Innovation Solution
A palletizing system utilizing depth cameras to detect box sizes and stacking patterns, generating height maps, and employing intelligent algorithms to determine stable placement postures and orientations for robotic arms, ensuring consistent and space-efficient stacking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional robotic arms with fixed palletizing algorithms are used, then reliable and accurate execution is achieved for fixed-size boxes, but unstable placement and inefficient palletizing occur when box sizes are inconsistent
Solution Approach 1:
The system dynamically adjusts the palletizing strategy based on real-time detection of box dimensions. The control device receives size information from sensors and automatically modifies placement patterns, making the system adaptable to varying box sizes while maintaining placement stability through algorithmic optimization.
Solution Approach 2:
The invention changes the parameter of box size from a fixed input to a variable input that the system can detect and respond to. By using sensors to detect actual box dimensions and adjusting placement parameters accordingly, the system achieves both adaptability to different sizes and reliability in placement stability.
2Reliability
If manual palletizing is performed to handle inconsistent box sizes, then stable placement can be achieved, but productivity and efficiency are reduced
Solution Approach 1:
The system performs self-service by automatically detecting box sizes and determining optimal placement positions without manual intervention. The control device processes size information and executes placement decisions autonomously, maintaining high productivity while ensuring stable placement through intelligent algorithms.
Solution Approach 2:
The system implements feedback by using sensors to detect actual box dimensions and feeding this information back to the control device, which then adjusts placement strategies accordingly. This closed-loop approach enables automatic adaptation to size variations while maintaining placement stability and high productivity.
3Device complexity
If fixed-size box assumptions are used in palletizing algorithms, then simple control is maintained, but the system cannot handle unknown or variable incoming materials
Solution Approach 1:
The system performs preliminary detection of box sizes before placement decisions are made. Sensors scan incoming boxes to obtain dimensional information, which the control device uses to pre-calculate optimal placement positions. This preliminary action maintains control simplicity by automating the detection and decision-making process.
Solution Approach 2:
The invention replaces complex mechanical measurement systems with sensor-based detection and software-based size determination. The control device uses detection information to calculate placement positions algorithmically, substituting mechanical complexity with intelligent control that maintains simplicity while handling variable materials.
Data Source
AI summary
A palletizing method includes: detecting, by one or more depth cameras, a stacking pattern on a pallet; generating a height map matrix based on the stacking pattern, wherein a value at each element of a plurality of elements in the height map matrix indicates a height of the stacking pattern at a position on the pallet corresponding to the element; for each box of the one or more boxes: traversing the elements in the height map matrix to obtain a mask matrix generated based on hypothetical situations that a target vertex of the box rests on a position corresponding to each of the elements; determining reward function values corresponding to one or more of elements in the mask matrix with an element value of 1; determining a box number and a box placement posture corresponding to a largest reward function value of the reward function values.