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

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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.

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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.

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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

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50+

AI & ML
Engineers

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40+

AI & ML
Projects for
reputed Clients

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5 yrs

in AI & ML
Engineering

Awarded as Winner among 1000 contestants at TechSHack Hackathon

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