Mobile Robot Monitoring Using Spatio-Temporal Object Inventory
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Solution Overview
Problem
Existing techniques for tracking object positions and orientations in environments using mobile robots often fail to utilize spatio-temporal object inventories effectively, leading to inefficient resource allocation and inadequate monitoring parameters for future movements.
Innovation Solution
Generating a spatio-temporal object inventory based on object observations from multiple mobile robots, which defines object poses and measurement times, and using this inventory to determine monitoring parameters that influence future robot movements, optimizing resource utilization by adjusting the number and path selection of robots based on predicted object movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mobile robots continuously monitor all areas to ensure accurate object tracking, then measurement precision is improved, but resource utilization deteriorates due to unnecessary deployment in low-activity areas
Solution Approach 1:
The system dynamically adjusts robot deployment and monitoring frequency based on real-time object movement detection. When object movements are detected in specific areas, robots are deployed to those areas; when no movements are detected, robots are redeployed or monitoring frequency is reduced, creating a dynamic adaptation to environmental conditions
Solution Approach 2:
The system performs preliminary analysis of object movement patterns using spatio-temporal inventories to predict future monitoring needs. By analyzing historical movement data and identifying patterns, the system proactively deploys robots to areas where object movements are likely to occur, rather than reactively responding to movements after they happen
2Reliability
If mobile robots are deployed to all areas to capture object movements, then object movement detection is improved, but resource consumption increases due to unnecessary robot deployment
Solution Approach 1:
The system applies different monitoring intensities to different spatial locations based on their specific needs. Areas with frequent object movements receive higher monitoring intensity with more robots and higher detection frequency, while areas with rare movements receive lower monitoring intensity, creating a non-uniform resource distribution optimized for local conditions
Solution Approach 2:
The system performs partial monitoring by focusing robot attention only on areas and time periods where object movements are detected or predicted, rather than performing exhaustive monitoring of all areas continuously. This partial action is sufficient to achieve reliable detection while minimizing resource consumption
3Measurement precision
If the system maintains a detailed spatio-temporal object inventory to track all object positions, then measurement precision is improved, but device complexity increases due to data processing requirements
Solution Approach 1:
The system extracts and stores only the essential spatio-temporal data elements needed for monitoring purposes - specifically object positions, timestamps, and movement events - rather than maintaining complete detailed inventories of all object attributes. This extraction of critical information maintains tracking precision while reducing data processing complexity
Data Source
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AI summary
Methods, apparatus, systems, and computer-readable media are provided for generating a spatio-temporal object inventory based on object observations from mobile robots and determining, based on the spatio-temporal object inventory, monitoring parameters for the mobile robots for one or more future time periods. Some implementations relate to using the spatio-temporal object inventory to determine a quantity of movements of objects that occur in one or more areas of the environment when one or more particular criteria are satisfied - and using that determination to determine monitoring parameters that can be utilized to provide commands to one or more of the mobile robots that influence one or more aspects of movements of the mobile robots at future time periods when the one or more particular criteria are also satisfied.