ARISTA : DEPLOYMENT OF SPOT ROBOT FOR PUBLIC SAFETY

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  USE CASE

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  COMMUNICATION MODEL

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  TECH STACK USED

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  WORKFLOW

What it does

SPOT would be equipped with various sensors, cameras, and LIDAR technology to perform inspections in hazardous environments. A network of SPOT bots will be deployed in a within a 2.5 – 3-mile radius surrounding a particular infrastructure for security and surveillance tasks, patrolling areas and providing real-time video feeds to human operators. It can be used to monitor large facilities, industrial sites, or public events, enhancing security effort.

These network of SPOT robots will be used to inspect and collect data/mages for analysis, tracking suspects, and gathering crucial intelligence in high-risk environments thus maintaining situational awareness without putting officers in harm's way.

They will be providing real-time video feeds . If it detects any malicious activity, the SPOT will act as the first respondent and deploy non-lethal measures by sending a distress signal to the closest law enforcement officer/authority who’d be able mitigate the situation effectively. Consequently, the other SPOT bots in the network would also be alerted. Its ability to provide real-time situational awareness without putting officers at risk is a significant advantage.

How we built it

• Together.ai : Used llama to enable conversations and consensus among agents
• MindsDB : Database is stored in postgres (render). The database is imported to Mindsdb. The sentiment classifier is trained with the help of demo data and the sentiments which are retrieved from every agent allows us to understand the mental state of every bot
• Reflex : UI for visualization of statistical measures of the bots -Intel : To train mobilevnet for classifying threats
• Intersystems : To Carry on Battery Life forecasting for the agent to enable efficient decisions

Built With

• postgresql
• python