Robotic Grasp Assessment Using Sensor Feedback in Warehouses
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Solution Overview
Problem
Robotic picking systems face challenges in reliably grasping all types of items, necessitating the development of methods and systems to autonomously assess and improve their grasping techniques.
Innovation Solution
A method and system that involves issuing commands to a robotic manipulator to attempt grasping an item, analyzing data from sensors to determine success, and incorporating corrective actions based on the received data, using various end effectors such as hand, suction, or adhesive devices, and integrating with warehouse management systems for item retrieval and transportation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robotic manipulators are deployed to perform picking tasks in warehouse environments, then productivity and cost-effectiveness are improved, but the reliability of grasping diverse items deteriorates
Solution Approach 1:
The system employs sensor devices (cameras, force sensors, torque sensors) to collect data during grasp attempts and provides feedback to the coordination system. This feedback loop enables the system to analyze sensor data, determine grasp success or failure, and adjust grasping strategies accordingly, thereby improving reliability while maintaining productivity
Solution Approach 2:
The robotic system autonomously assesses its own grasping performance by analyzing sensor data without human intervention. The coordination system automatically determines whether a grasp was successful and issues corrective instructions when needed, enabling the system to self-correct and improve its grasping capability across diverse items
2Reliability
If autonomous assessment systems are implemented to improve grasping reliability, then device complexity increases
Solution Approach 1:
The coordination system serves multiple functions: it manages robotic manipulator operations, processes sensor data from various sources (cameras, force sensors, torque sensors), determines grasp success, and issues corrective instructions. This multi-functional approach consolidates complexity into a single coordination layer rather than distributing it across multiple specialized systems
Solution Approach 2:
The coordination system acts as an intermediary between the robotic manipulators and the warehouse management system. It receives commands from the warehouse management system, executes grasping tasks, analyzes sensor data, and reports results, thereby simplifying the overall system architecture by providing a centralized control layer
Data Source
AI summary
Methods and systems for assessing a robotic grasping technique. The system in accordance with various embodiments may include a warehouse management system for retrieving and storing items, a robotic manipulator for grasping an item, and analysis module configured to receive data regarding a first grasp attempt by the robotic manipulator and analyze the received data to determine whether the robotic manipulator successfully grasped the item.