Autonomous Indoor Mobility

Business Impact

  • Of late, the deployment of unmanned robots in workplaces is on the rise. There are already growing concerns about the safety profile of the worker-robot interaction space.
  • The Deep Learning techniques will assist businesses to overcome these challenges in handling autonomous robots in workplaces like inventories, manufacturing sectors, restaurants, and warehouses.
  • 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.
  • Our solution is aimed at avoiding worker-robot and robot-robot collisions while on the move inside the workplace.
  • We have used Deep Learning technology to address the collision issue and overcome the same. Our solution is integrated with Visual Odometry to determine GPS coordinates.
  • The integration of GPS coordinates helped autonomous robots with a much better understanding of their surroundings to take necessary actions.
  • By implementing this solution, our client was able to reduce the worker-robot collision scenario to an overwhelming 95% and the model is still improving.

Technology Stack

Solution Approach


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.

Trusted and Proven Engagement Model

  • A nondisclosure agreement (NDA) is signed to not disclose any sensitive information revealed over the course of doing business together.
  • Our NDA-driven process is established to keep clients’ data and IP safe and secure.
  • The solution discovery phase is all about knowing your target audience, writing down requirements, and creating a full scope for the project.
  • This helps clarify the goals, and limitations, and deliver quality products & services.
  • Our engagement model defines the project size, project development plan, duration, concept, POC etc.
  • Based on these scenarios, clients may agree to a particular engagement model (Fixed Bid, T&M, Dedicated Team).
  • The SOW document shall list details on project requirements, project management tools, tech stacks, deliverables, milestones, timelines, team size, hourly/monthly rate cards, billable hours and invoice details.
  • On signing the SOW, an official project kick-off meeting shall be initiated.
  • Our implementation approach, ecosystem, tools, solutions modelling, sprint plan, etc. shall be discussed during this meeting.

Awards & Recognition

Awarded Bronze Trophy at CII National competition on Digitization, Robotics & Automation (DRA) – Industry 4.0






Projects for
reputed Clients


5 yrs

in AI & ML

Awarded as Winner among 1000 contestants at TechSHack Hackathon

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