Robotic Manipulator Training for Adaptive Item Grasping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing robotic picking systems struggle to reliably grasp various items due to differences in size, shape, material, and weight, leading to inefficiencies and increased costs in warehouse and distribution environments.
Innovation Solution
A method and system for training robotic manipulators that involve data collection from sensors, generation of grasping strategies, and iterative evaluation and adjustment of these strategies based on execution feedback, using a planning module to optimize grasping parameters such as robotic position, pre-grasp manipulations, and image processing techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a robotic picking system is deployed to automate item handling, then productivity and cost-effectiveness are improved, but the system fails to reliably grasp items with varying characteristics
Solution Approach 1:
The system performs preliminary sensing and characterization of items before grasping attempts. Sensors scan items to detect their characteristics (size, shape, material, weight) in advance, allowing the control system to pre-calculate appropriate grasping parameters and strategies before the robotic manipulator attempts to grasp the item.
Solution Approach 2:
The system implements feedback loops where sensor data from grasping attempts is continuously fed back to the planning module. The sensors detect whether items were successfully grasped and provide feedback on grasping quality, allowing the system to iteratively refine and adjust grasping strategies to improve reliability across different item types.
2Device complexity
If the robotic manipulator uses a fixed grasping strategy, then the system complexity is reduced, but the system cannot adapt to items with different size, shape, material, and weight
Solution Approach 1:
The grasping strategy is made dynamic rather than static. The control system adjusts grasping parameters in real-time based on sensor feedback and item characteristics. The system can modify force applied, gripper position, approach angle, and other parameters dynamically to adapt to different items while maintaining a relatively simple overall system architecture.
Solution Approach 2:
The system changes physical and operational parameters of the grasping strategy based on detected item properties. When sensors detect variations in item size, shape, material, or weight, the planning module automatically adjusts corresponding grasping parameters such as grip force, manipulator position, and approach velocity to optimize grasping for each specific item type.
3Reliability
If multiple sensors and iterative training are implemented, then grasping reliability is improved, but the system complexity and training time increase
Solution Approach 1:
The sensor system is designed with multi-functionality to justify its complexity. The same sensors serve multiple purposes: item detection, characterization (size, shape, material, weight), grasping verification, and training data collection. This universal use of sensors improves grasping reliability without proportionally increasing system complexity.
Solution Approach 2:
The system performs self-training and self-improvement through automated iterative learning. The robotic manipulator autonomously executes grasping attempts, receives feedback from sensors, and automatically adjusts its strategies without requiring extensive manual programming or intervention. This self-service capability improves reliability while minimizing the operational complexity burden.
Data Source
AI summary
Methods and systems for training a robotic manipulator. The system may include one or more sensor devices and a robotic manipulator for executing an item grasping strategy to grasp an item. The system may further evaluate the item grasping strategy to determine whether the strategy was successful.