Depalletization Vision Rail With Laser Profiling for Edge Picking
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Solution Overview
Problem
Robotic depalletization systems face challenges with vision systems that are difficult to access for maintenance, require high-resolution lenses, and incur high costs due to the need for superior quality sensors to operate over variable working distances.
Innovation Solution
A robotic depalletization system with a vision system comprising a first rail and laser profilometers that measure geometric dimensions of objects, a processor to identify object edges, and a robotic arm for precise picking, along with 2D image capturing devices for real-time monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the vision system is installed at high elevated positions to monitor objects on the pallet, then the vision system can cover a larger area, but the system becomes difficult to access for adjustments or maintenance
Solution Approach 1:
The vision system is mounted on a movable cart with casters rather than being fixed at high elevation, allowing it to be dynamically repositioned for maintenance and then moved into position for operation. This dynamic mounting solution resolves the contradiction between coverage area and accessibility.
2Area of stationary object
If the vision system is placed farther away from the products to monitor the entire pallet, then the system can cover more area, but higher resolution and finer quality lenses and sensors are required to accurately detect small feature sizes
Solution Approach 1:
The vision system can dynamically adjust its working distance by moving the cart closer to or farther from the pallet. This allows optimization of the balance between coverage area and measurement precision depending on the specific operational requirements.
Solution Approach 2:
The system changes the working distance parameter dynamically, allowing the vision system to operate at optimal distances for different measurement tasks, thereby resolving the contradiction between area coverage and detection accuracy.
3Adaptability or versatility
If fixed position vision systems operate over variable working distances, then the system can accommodate different pallet configurations, but the effectiveness and reliability of the vision system becomes complicated
Solution Approach 1:
Rather than relying on fixed-position systems to handle variable distances, the system dynamically adjusts its position to maintain optimal working distance. This preserves reliability while accommodating different pallet configurations through positional adaptability rather than optical complexity.
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 accurate and efficient object identification, reduces system footprint, and minimizes unnecessary vertical lift motions, thus enhancing the depalletization process.
Implementation Method 1
a plurality of laser profilometers coupled to the first rail. The plurality of laser profilometers is configured to measure geometric dimensions of the plurality of objects
Data Source
AI summary
A robotic depalletization system is disclosed. The robotic depalletization system comprises a robotic arm configured to pick the plurality of objects. Further, the robotic depalletization system comprises a vision system. The vision system comprises a first rail, a first actuator, and a plurality of laser profilometers. The first actuator is configured to move the first rail in a horizontal direction over the plurality of objects. The plurality of laser profilometers is configured to measure geometric dimensions of the plurality of objects. The vision system comprises at least one processor that is configured to identify locations of edges of each of the plurality of objects based on the geometric dimensions of the plurality of objects and generate one or more signals to control the robotic arm to pick the plurality of objects based on the identified locations of the edges of each of the plurality of objects.