Service Automation in Restaurants | Washington | Los Angeles | New York

Artificial Intelligence & Service Automation in Restaurants -overview

  • Human-robot collaboration is becoming more frequent in contemporary activities like business and other research contexts. No matter how simple or complex the task, it needs some element of automation.
  • The development of robotic arms is a crucial step in achieving this goal. However, safety is still a problem in this scenario. One of the most frequent issues in this cooperative sensing is human-robot collision.
  • Even if the benefits of automation in the activities have been highlighted, there is a continuous need to prevent or even prevent harm to the contributing agents.

  • Collision detectors are critical ingredients for facilitating safe human contact, avoiding collisions, or even hastening the accident investigation process.
  • In recent years, a multitude deep learning-based theories have arisen.
  • While managing autonomous robots in situations like warehouses, factories, restaurants, and chain outlets, deep learning technologies can help enterprises in overcoming these difficulties.
  • The collision problem will be addressed and solved via deep learning. Our solution includes Visible Object tracking, which is used to determine GPS coordinates.
  • GPS coordinates are included in the to provide automated systems a better understanding of their environment and to help them make good decisions.


  • Improved safety
  • Improved efficiency and productivity.
  • Greater flexibility.
  • Enhanced precision

How we Helped Restaurants with our robot-human collision avoidance using Deep learning?

Solution Approach

  • We have helped one of the leading restaurants in the USA to overcome the challenge of managing autonomous Robots collision while serving foods in their restaurant.
  • Reinforcement Learning has become a primary driver for autonomous robots, be it person bots that acts as smart pets like Anki’s Cozmo or fancy robots that are present in hotels for food delivery.
  • The current state or location of these agents is harder to describe with the use of GPS coordinates. Since these agents are meant to perform in a given closed environment, We can use visual information they are collected from the agent’s camera to predict the current state coordinates.
  • This technique is called Visual Odometry wherein we use Convolution and Recurrent Neural Networks to process the 3D frame to determine the current location.


Market size: Collision Avoidance

The global collision avoidance sensor market size was valued at $4.00 billion in2022, and project to reach $12.25 billion by2030, Registering of CARG of 11.9% from 2021 to 2030.


Connect With Us!