Manufacturing Environment Survey Routing Around Obstacles and Charging Limits
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Dynamic changes and obstacles in manufacturing environments compromise the accuracy of digital twins, hindering effective data collection by robotic systems.
Innovation Solution
A method and system that segment nodes into communities with community centroids, determine community types based on charging station distances, and generate intercommunity routes using performance constraints to efficiently survey environments with carrier robots and drones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robotic systems traverse the environment to capture scene information, then data collection is performed, but obstacles inhibit accurate data collection and dynamic changes compromise digital twin accuracy
Solution Approach 1:
The environment is segmented into multiple communities based on spatial proximity and connectivity, with each community surveyed independently. This segmentation allows the system to adapt to obstacles by working within smaller, more manageable regions while maintaining overall environmental coverage through multiple intercommunity routes.
Solution Approach 2:
The system dynamically adjusts surveying routes and community assignments based on real-time energy levels, obstacle locations, and environmental changes. Robots can transition between communities and recharge at charging stations, enabling continuous adaptation to changing conditions while maintaining surveying accuracy.
2Loss of information
If robots survey the entire environment continuously, then complete data collection is achieved, but energy consumption increases and time constraints are violated
Solution Approach 1:
The surveying task is divided into community-level subtasks, allowing robots to focus on local areas rather than traversing the entire environment continuously. This segmentation reduces energy consumption per task while maintaining overall data completeness through coordinated multi-robot operations across different communities.
Solution Approach 2:
Communities are pre-identified and charging stations are pre-positioned throughout the environment. Robots can efficiently navigate to their assigned communities and return to known charging locations, reducing unnecessary traversal and energy consumption while ensuring complete coverage over time.
3Duration of action of moving object
If charging stations are positioned to optimize energy management, then robot operational duration increases, but system complexity increases
Solution Approach 1:
Charging stations are strategically positioned within or near each community based on local surveying needs and robot traffic patterns. This localized approach optimizes energy management for each community independently while keeping the overall system manageable through modular community structures.
Solution Approach 2:
Community centroids serve as intermediary coordination points between charging stations and surveying operations. The centroids aggregate surveying tasks and route robots to appropriate charging stations, simplifying the overall system architecture by introducing a hierarchical layer of abstraction.
4Productivity
If multiple intercommunity routes are generated, then route optimization for energy and time constraints is achieved, but computational complexity increases
Solution Approach 1:
Route optimization is performed separately for each community and then integrated through intercommunity connections. This segmented approach reduces computational complexity by breaking down the global routing problem into smaller local problems that can be solved independently and combined.
Solution Approach 2:
The system generates multiple dynamic intercommunity routes that can be selected based on real-time conditions such as robot energy levels, current community surveying status, and obstacle locations. This dynamic route selection improves surveying efficiency while managing computational complexity through on-demand route generation rather than pre-computing all possible routes.
Data Source
AI summary
A method for surveying an environment includes segmenting a plurality of nodes into a plurality of communities. The method includes identifying a plurality of community centroids based on the plurality of communities, where each community centroid from among the plurality of community centroids is associated with one community from among the plurality of communities. The method includes determining, for each community centroid from among the plurality of community centroids, a community type of the community centroid, where the community type is one of a primary type and an auxiliary type, and where the community type is based on a distance between the community centroid and one or more charging stations. The method includes generating a plurality of intercommunity routes based on the plurality of community centroids, the one or more charging stations, the community type, and one or more performance constraints.