Robotic Item Handling Mode Selection Using 3D Point Clouds
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Solution Overview
Problem
In material handling environments, manual loading and unloading of vehicles is costly, inefficient, and physically demanding, necessitating an automated system for quickly and effectively handling bulk quantities of stacked cases and cargo.
Innovation Solution
A robotic item handler system that uses sensor devices to capture three-dimensional images, generates combined point cloud data, and employs a machine learning model to decide between operating modes, such as picking or sweeping, to optimize item handling based on probability distributions and pre-defined heuristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual labor is used to load or unload vehicles, then flexibility and adaptability are maintained, but labor costs increase and productivity decreases
Solution Approach 1:
The robotic item handler autonomously performs loading and unloading operations without human intervention. The system uses sensors to detect items, determines optimal handling modes (sweeping, picking, placing), and executes operations automatically, enabling the system to serve itself and eliminating the need for manual labor while maintaining high productivity
Solution Approach 2:
The patent replaces manual mechanical operations with an automated robotic system that uses sensors, processors, and actuators. The robotic item handler substitutes human operators and manual mechanical processes with an integrated automated system that can perform multiple operations (sweeping, picking, placing) based on real-time environmental assessment
2Adaptability or versatility
If a single operating mode is used for item handling, then system complexity is reduced, but adaptability to different item configurations decreases
Solution Approach 1:
The robotic item handler dynamically selects from multiple operating modes (sweeping, picking, placing) based on real-time environmental conditions and item configurations. The system transitions between different handling strategies depending on the situation, making the system adaptable without requiring complex reconfiguration, as the mode selection is driven by sensor data and processing algorithms
Solution Approach 2:
The robotic item handler is designed to perform multiple functions through a single integrated system. The same robotic platform can sweep items, pick individual items, and place them in various locations, making it a universal handling system that adapts to different tasks rather than requiring separate specialized equipment for each operation type
Data Source
AI summary
A method for controlling a robotic item handler is described. The method includes constructing a machine learning model based on a combined point cloud data as an input to a convolution neural network, outputting a decision classification indicative of a first probability associated with a first operating mode and a second probability associated with a second operating mode, selecting one of the first operating mode and the second operating mode of the robotic item handler based on the decision classification outputted by the machine learning model, and operating the robotic item handler according to the selection of the one of the first operating mode and the second operating mode.