Hazard Response Robots Using Digital Twins for Rare Hazard Detection
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Solution Overview
Problem
Existing systems struggle to effectively detect and mitigate hazards in complex environments due to the rarity of hazards and dynamic nature of objects, leading to inefficiencies in simulation and high costs of brute-force testing.
Innovation Solution
Utilizing a swarm of cooperative autonomous mobile robots (AMRs) equipped with sensors and machine learning capabilities to explore environments, generate shared environment models, and implement path planning algorithms to identify and mitigate hazards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simulations are used to detect hazardous situations, then hazard detection capability is improved, but system complexity limits the effectiveness of such simulations
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the physical environment that replicates hazard scenarios. This virtual model allows hazardous situations to be studied and detected without the complexity of full-scale physical simulations, enabling accurate hazard detection while managing system complexity through simplified virtual representations.
2Reliability
If brute-force testing is used to detect hazards, then comprehensive hazard coverage is improved, but cost increases significantly
Solution Approach 1:
The patent performs preliminary hazard identification using the digital twin model before conducting physical tests. By pre-identifying potential hazard scenarios in the virtual environment, the system reduces the number of expensive brute-force physical tests needed, thereby maintaining comprehensive hazard coverage while significantly reducing testing costs.
Solution Approach 2:
The digital twin serves as a virtual copy that replaces expensive physical brute-force testing. Hazard scenarios can be repeatedly tested in the virtual model at minimal cost, providing comprehensive hazard coverage without the high costs associated with physical re-testing.
3Device complexity
If traditional detection methods are used, then system simplicity is maintained, but the rarity and dynamic nature of hazards make detection difficult
Solution Approach 1:
The digital twin creates a virtual replica of the environment where rare and dynamic hazards can be observed and analyzed without requiring complex physical detection systems. This copying approach maintains relative system simplicity while dramatically improving the ability to detect and study rare hazardous events through repeated virtual simulations.
Data Source
AI summary
Techniques are disclosed for the exploration of environments for the estimation and detection of hazards or near hazards within the environment and the mitigation of hazards therein. The exploration of the environment and mitigation of hazards therein may use one or more autonomous agents, including a hazard response robot. The estimation of the hazards may use a policy learning engine, and the hazards may be detected, and the associated risks therefrom, may be determined using a hazard estimation system.