Mobile Environment Modeling for Dynamic Warehouse Coverage
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Solution Overview
Problem
Conventional methods for generating environmental models in industrial settings require significant installation effort and are not dynamically adjustable, limiting their effectiveness in capturing dynamic changes and adapting to varying environments.
Innovation Solution
Utilizing a swarm of small, agile autonomous mobile reconnaissance units (Micro-ARMs) equipped with sensors to dynamically update the environmental model by moving throughout the environment, combined with neural networks for rapid 3D reconstruction from 2D images, allowing decentralized data processing and real-time updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If permanently installed infrastructure sensors are used to generate environmental models, then comprehensive environmental coverage is achieved, but installation effort and system complexity increase significantly
Solution Approach 1:
The patent transforms the static sensor infrastructure into a dynamic system by deploying mobile reconnaissance units that can move throughout the environment. These units dynamically adjust their positions to capture environmental data from multiple locations, replacing the need for numerous fixed sensors while maintaining comprehensive coverage through coordinated movement patterns.
Solution Approach 2:
The patent uses neural radiance fields to create virtual copies of the physical environment. By capturing images from mobile reconnaissance units and processing them through NeRF algorithms, the system generates accurate 3D environmental models without requiring physical sensors at every location, thus reducing installation complexity while preserving measurement precision.
2Area of stationary object
If fixed camera grids are installed in high-bay warehouses to capture narrow aisles, then complete area coverage is achieved, but installation cost and maintenance complexity increase
Solution Approach 1:
The mobile reconnaissance units serve multiple functions: they capture images for environmental modeling, navigate autonomously through narrow aisles, and can be deployed or repositioned as needed. This multi-functionality replaces the need for dedicated fixed cameras in each location, reducing overall system cost while maintaining complete area coverage.
Solution Approach 2:
Instead of static cameras fixed in grid patterns, the patent employs dynamic mobile units that can adapt their positions and orientations. These units move through narrow aisles and adjust their capture angles to cover areas that would require numerous fixed cameras, thereby reducing installation cost while preserving comprehensive coverage.
3Adaptability or versatility
If mobile sensors are used instead of fixed infrastructure sensors, then installation effort is reduced and flexibility increases, but measurement consistency and coverage reliability may deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where mobile reconnaissance units continuously report their positions, orientations, and captured images to a central system. This feedback enables real-time adjustments to navigation paths and capture strategies, ensuring that measurement consistency is maintained despite the mobile nature of the sensors. The system adapts to maintain reliable coverage based on incoming data quality and environmental conditions.
Solution Approach 2:
The patent replaces mechanical fixed sensor mounts with software-based autonomous navigation systems. Mobile reconnaissance units use computer vision, SLAM (Simultaneous Localization and Mapping), and path planning algorithms to maintain measurement consistency without physical infrastructure. This substitution provides flexibility while preserving reliability through intelligent software control.
4Speed
If numerous fixed sensors are deployed to capture dynamic environmental changes, then real-time monitoring capability is improved, but system complexity and power consumption increase
Solution Approach 1:
The patent uses mobile reconnaissance units that dynamically move through the environment rather than maintaining numerous fixed sensor positions. This dynamic approach allows the system to concentrate sensing resources on areas with changing conditions, reducing overall power consumption while maintaining real-time monitoring capability through targeted data collection and efficient navigation.
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
Enables a highly accurate, up-to-date environmental model with reduced installation effort, enhanced flexibility, and improved safety by avoiding collisions with personnel, while optimizing route planning and reducing the risk of accidents.
Implementation Method 1
The sensors, in particular cameras and/or LIDARs, record their respective local sub-areas of the environment
Implementation Method 2
The sensors, in particular cameras and/or LIDARs, record their respective local sub-areas of the environment
Implementation Method 3
Neural Radiance Fields (NeRF) is a technology based on deep neural networks that can be used to reconstruct a 3D scene
Data Source
Figure 1~2
Figure 3~4
AI summary
A method for generating an environmental model of an environment (24) in an industrial or logistics facility is described, wherein a multitude of sensors (18) distributed throughout the environment (24) detect a respective local sub-area of the environment (24), and the environmental model is assembled from this data. A multitude of autonomous mobile reconnaissance units (12), in particular autonomous mobile robots, move within the environment (24), and at least some of the sensors (18) are part of a mobile reconnaissance unit (12) and thus detect the environment (24) at changing locations.