Industrial Truck Obstacle Mapping for Static-Dynamic Separation
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Solution Overview
Problem
Existing obstacle detection systems in industrial trucks for order picking fail to differentiate between static and dynamic obstacles, leading to unnecessary evasive movements and incomplete environmental coverage, causing inefficiencies and potential collisions.
Innovation Solution
A method that uses a sensor-connected control device to create a map of the environment, weighting static objects higher and dynamic objects lower, with a virtual sensor providing obstacle data to the vehicle controller, preventing unwanted evasive maneuvers and enhancing detection range and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If optical sensors are used to detect all objects in the monitoring area, then complete environmental coverage is achieved, but unnecessary evasive movements occur due to dynamic objects like order pickers
Solution Approach 1:
The system dynamically adjusts the weighting of detected objects based on their persistence in the monitoring area. Static obstacles maintain high weight values while dynamic objects like order pickers have their weight values reduced over time as they move through the area, allowing the system to adapt its response to different object types without manual reconfiguration
Solution Approach 2:
The patent changes the parameter of object weight in the data structure based on temporal persistence. By continuously updating weight values - increasing for persistent static objects and decreasing for transient dynamic objects - the system transforms the raw sensor data into differentiated obstacle categories that trigger appropriate response behaviors
2Area of stationary object
If the sensor monitors the entire monitoring area, then comprehensive environmental awareness is achieved, but detection precision is reduced due to inability to differentiate static and dynamic objects
Solution Approach 1:
The monitoring area is segmented into multiple detection zones, and objects are segmented into different weight categories based on their spatial-temporal characteristics. This segmentation allows the system to apply different evaluation criteria to different regions and object types simultaneously, achieving both comprehensive coverage and precise differentiation
Solution Approach 2:
The system uses feedback from continuous sensor monitoring to update object weight values in real-time. By comparing the presence and position of objects across multiple measurement cycles, the system generates feedback that distinguishes between static obstacles (consistent presence) and dynamic objects (changing presence), thereby achieving precise obstacle type differentiation across the entire monitoring area
3Reliability
If the truck evades all detected obstacles, then collision safety is improved, but time is lost due to unnecessary lateral movements
Solution Approach 1:
Instead of applying full evasive action to all detected objects, the system applies partial or selective evasive action based on object weight thresholds. High-weight static obstacles trigger full evasive maneuvers, while low-weight dynamic objects trigger no evasive action, eliminating unnecessary lateral movements and time loss while maintaining collision safety for critical 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 reduces unnecessary evasive movements by distinguishing between relevant static and non-relevant dynamic obstacles, improving navigation efficiency and avoiding collisions, while providing comprehensive environmental awareness.
Implementation Method 1
the industrial truck has a sensor for monitoring the environment, which is connected to a control device and detects a monitoring area by evaluating the data from the sensor
Data Source
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AI summary
In a method for detecting obstacles (3) in the vicinity of an industrial truck (1), in particular an industrial truck (1) provided for order picking, the industrial truck (1) having a sensor (4) for monitoring the surroundings, which is equipped with a Control device is connected, and a monitoring area (9) is detected by evaluating the data from the sensor (4), objects (7,8) detected in the data in the area surrounding the industrial truck (1) are entered on a map (10) and is continuously the monitoring area (6) is recorded again, with objects (7) at the same absolute position on the map being weighted higher and with objects (8) missing at the same position, the weighting of an entered object (8) being reduced until it is deleted of the object (8).