Edge Trajectory Map Generation for Logistics Coordination
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Solution Overview
Problem
In logistics environments like warehouses, coordinating the movement of multiple mobile devices such as forklifts is challenging due to concurrent operations and unknown positions, leading to potential accidents and complexities in decision-making.
Innovation Solution
A method involving segmenting node trajectories based on collected data, determining initial trajectories using subtrajectories and frequencies, and generating a map by associating attribute and event data with these trajectories, facilitating real-time decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple mobile devices operate concurrently in a logistics environment, then productivity increases, but the complexity of coordinating movement and ensuring safety increases
Solution Approach 1:
The patent segments the complex coordination problem by creating individual trajectory maps for each mobile device (e.g., forklift) rather than managing all devices simultaneously. Each device has its own trajectory map that stores historical path data, allowing independent management of each device's movement while reducing overall system complexity.
Solution Approach 2:
The patent implements preliminary action by pre-calculating and storing trajectory maps before actual operation occurs. The system collects and stores historical trajectory data in advance, creating a reference framework that guides real-time decision-making and reduces the computational burden during concurrent operations.
2Reliability
If real-time position data is collected for all mobile devices, then safety monitoring improves, but the amount of data processing and response time increases
Solution Approach 1:
The patent segments the safety monitoring approach by maintaining separate trajectory maps for each device rather than processing a single unified dataset. This segmentation allows the system to query and process only the relevant trajectory data for specific devices, reducing overall data processing volume and improving response time while maintaining comprehensive safety monitoring.
Solution Approach 2:
The patent uses trajectory maps as copied representations of historical movement patterns. Instead of continuously processing raw position data, the system queries pre-computed trajectory maps that contain summarized path information, significantly reducing real-time processing requirements while maintaining accurate safety monitoring capabilities.
3Loss of information
If trajectory data is collected and processed for multiple devices, then the ability to reason about typical trajectories improves, but the computational resources and data storage requirements increase
Solution Approach 1:
The patent extracts and stores only the essential trajectory information (path sequences) in trajectory maps rather than retaining all raw sensor data and processing metadata. This extraction approach maintains high-quality trajectory information for analysis while significantly reducing the volume of data that needs to be stored and processed computationally.
Solution Approach 2:
The patent performs preliminary data processing and compression by pre-computing and storing trajectory maps in a condensed format. Historical trajectory data is processed in advance to create efficient data structures that can be quickly queried during real-time operation, reducing the computational resources needed for ongoing analysis while preserving information quality.
Data Source
AI summary
Techniques for edge-enabled trajectory map generation are disclosed herein. For example, a method can include segmenting one or more node trajectories based on data collected at a node, determining one or more initial trajectories based on the segmented node trajectories, and generating a map including trajectories generated by associating attribute data and event data with the initial trajectories.