Forklift Echo Timing for Detecting Objects Behind Transparent Barriers
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Solution Overview
Problem
Existing methods for object detection in industrial trucks, such as template matching using 3D cameras, fail when objects are obscured by partially transparent obstacles, leading to incorrect recognition of pallets and free spaces.
Innovation Solution
The method involves emitting detection signals and evaluating signal echoes based on their solid angle and reception time, prioritizing the last arriving echo to distinguish objects from partially transparent obstacles, allowing for reliable recognition and control of industrial trucks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If template matching using 3D cameras is used for object detection, then object recognition can be performed, but recognition fails when objects are obscured by partially transparent obstacles such as films or segment curtains
Solution Approach 1:
The patent segments the detected scene into multiple depth layers by analyzing time-of-flight data. Objects at different distances from the sensor are separated into distinct depth ranges, allowing the system to identify and prioritize the closest objects (such as pallets) while filtering out background elements. This segmentation enables reliable detection even when partially transparent obstacles are present, as the system can distinguish between objects at different depths rather than treating them as a single merged image.
Solution Approach 2:
The patent transitions from 2D image-based template matching to 3D spatial analysis using time-of-flight measurement. By adding the time dimension (distance measurement) to the traditional 2D camera data, the system creates a point cloud representation that includes depth information. This dimensional enhancement allows the system to detect objects behind partially transparent films by measuring their actual distance from the sensor, overcoming the limitations of 2D visual occlusion.
2Reliability
If LIDAR systems send out laser pulses and evaluate echoes to detect obstacles hidden by fog or smoke, then obstacle detection in adverse conditions improves, but the system complexity increases
Solution Approach 1:
The patent integrates multiple detection functions into a single sensor unit that combines 3D camera capabilities with time-of-flight measurement. This multi-functional sensor can perform both visual template matching and depth-based object detection simultaneously, eliminating the need for separate LIDAR hardware while achieving similar penetration capabilities through partially transparent obstacles. The unified sensor reduces system complexity compared to using dedicated LIDAR systems.
Solution Approach 2:
The patent uses the existing 3D camera infrastructure already present in industrial trucks for automated picking operations. Instead of introducing new LIDAR hardware, the system repurposes the time-of-flight measurement capability of the existing camera sensor to create depth information. This approach copies the successful deployment model of 3D cameras while extending their functionality to handle partially transparent obstacles.
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
This approach enables the industrial truck to reliably detect objects behind partially transparent films and segment curtains, improving object recognition accuracy and enabling safe navigation and pallet pickup.
Implementation Method 1
Emitting a detection signal by means of a transmitting device of the industrial truck, detecting the detection signal reflected from objects in the environment as a signal echo by means of a receiving device of the industrial truck
Implementation Method 2
assigning a solid angle and a reception time to each of the signal echoes
Data Source
Figure 1a~1b
Figure 2a~2b
Figure 3a~3b
AI summary
The inventive method for detecting objects in a warehouse using a forklift truck comprises the following steps: transmitting a detection signal by means of a transmitter of the forklift truck, capturing the detection signal reflected by objects in the environment as a signal echo by means of a receiver of the forklift truck, assigning a solid angle and a time of reception to each of the signal echoes, checking whether the same solid angle has been assigned to several of the signal echoes if several signal echoes have been assigned to one solid angle, evaluating the signal echo that last arrived in the receiver according to the time of reception for this solid angle for the detection of the object, and detecting the object based on the signal echoes.