Artificial Intelligence & Service Automation in Restaurants

Artificial Intelligence & Service Automation in Restaurants — overview

• Collaboration between humans and robots is becoming more prevalent in modern activities, such as business and several research contexts. No matter how straightforward or difficult the activity, it requires some level of automation.
• Robotic arms are introduced, which is a significant step in achieving this objective. However, in this situation, safety is still an issue. Human-robot collision is one of the most common problems in this collaborative environment.
• While the effectiveness of automation in the activities has been highlighted, there is an increasing need to lessen or even prevent harm to the participating agents.

• In the scope of industry, collision detectors are crucial instruments for enabling safe human-robot collaboration, preventing collisions, or even expediting the accident relief process.
• Several related deep learning-based ideas have developed in recent years.
• The Deep Learning approaches can help companies get beyond these obstacles while handling autonomous robots in settings like warehouses, factories, restaurants, and retail stores.
• Deep Learning technique to address and solve the collision problem. GPS coordinates are calculated using Visual Odometry, which is integrated with our solution.
• GPS coordinates were incorporated to provide autonomous robots a much greater awareness of their environment and to assist them take the appropriate actions

Benefits

• 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.

Technology

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.