Suction End Effector Handling for Soft Products in Flexible Packaging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robotic systems face challenges in handling and packing dissimilar items, especially fragile products in flexible packaging, due to variability in size, weight, and packaging type, leading to instability and potential damage during palletization and distribution.
Innovation Solution
A robotic system equipped with 3D cameras, force sensors, and suction-based end effectors uses item type-specific models and dynamic libraries to identify and grasp items, employing strategies for stable placement and re-planning to ensure efficient packing and unpacking of non-homogeneous items, including those in plastic bags, using suction cups and human intervention when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a robotic gripper is used to handle items in flexible packaging, then automation is improved, but the packaging and product inside are crushed or damaged
Solution Approach 1:
The patent replaces traditional mechanical robotic grippers with a vision-guided system that uses suction cups or gentle contact methods. The vision system (cameras and image processing) identifies item locations and characteristics, allowing the robot to adjust its gripping force and method dynamically, substituting brute mechanical force with intelligent control and alternative physical principles like suction.
Solution Approach 2:
The system dynamically changes gripping parameters (force, contact point, gripper type) based on real-time vision data about the item's packaging material, size, and fragility. This allows the same robotic system to adapt its handling parameters for different products in flexible packaging, preventing crushing while maintaining automation.
2Productivity
If dissimilar items are packed efficiently in boxes and crates, then productivity is improved, but stability and weight distribution become difficult to maintain
Solution Approach 1:
The vision system performs preliminary identification and classification of items before packing begins. The system plans the entire palletization sequence in advance, determining optimal placement locations for each item based on its dimensions, weight, and fragility. This preliminary planning ensures stable weight distribution and stack integrity while maximizing packing efficiency.
Solution Approach 2:
The system applies different packing strategies to different regions of the pallet based on local requirements. Heavier items are placed at the bottom, lighter items at the top, and fragile items are positioned in protected locations. Each item's placement is optimized for its specific properties, maintaining overall stack stability while achieving high packing density.
3Reliability
If human workers manually select and stack items, then stability and item safety are improved, but productivity and efficiency decrease
Solution Approach 1:
The robotic system autonomously performs item identification, classification, and placement without continuous human intervention. The vision system independently detects item characteristics, the planning algorithm automatically determines optimal stacking sequences, and the robot executes the plan. This self-service capability maintains high reliability through intelligent decision-making while achieving industrial-scale productivity.
Solution Approach 2:
The system uses real-time vision feedback to monitor item placement accuracy and stack stability. Sensors detect the actual position and orientation of placed items, comparing them against the planned configuration. This feedback loop allows the system to make corrective adjustments, ensuring human-level reliability in stability assessment while maintaining automated speed and throughput.
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
The system effectively handles and packs fragile items without damage, ensuring stable palletization and efficient distribution by accurately identifying and manipulating items based on their attributes, improving throughput and reducing human intervention.
Implementation Method 1
A robotic system equipped with 3D cameras, force sensors, and suction-based end effectors uses item type-specific models and dynamic libraries to identify and grasp items
Data Source
AI summary
Techniques are disclosed to perform robotic handling of soft products in non-rigid packaging. In various embodiments, sensor data associated with a workspace is received. An action to be performed in the workspace using one or more robotic elements is determined, the action including moving an end effector of one of the robotic elements relatively quickly to a location in proximity to an item to be grasped; actuating a grasping mechanism of the end effector to grasp the item using an amount of force and structures associated with minimized risk of damage to one or both of the item and its packaging; and using sensor data generated subsequent to the item being grasped to ensure the item has been grasped securely. Control communications are sent to the robotic element via the communication interface to cause robotic element to perform the action.