12/27/2022 updated

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Robotics & Computer Vision Engineer

Hyderabad, India
India
Masters of Technology
Hyderabad, India
India
Masters of Technology

Profile attachments

CV - Polaki Babu

AlgorithmsComputer VisionComputer ProgrammingData StructuresGenetic AlgorithmMachine LearningRobotics Design and ProductionReinforcement LearningData ScienceDeep LearningInformation TechnologyOperational Systems
Being a graduate from Computer Science and Engineering and having my Masters pursued in the stream of Robotics, the rare blend of the hands-on experience in these streams of Robotics, Computer-vision and Computer Science is what primarily forms the crux of my expertise.

Have dealt with two major Computer vision projects where I played the multiple roles from the core developer of the project to Managing the whole project over the span of two years. Simultaneously, I gained experience in programming robotics having ROS
(Robotics Operating System) as a middle-ware.

Eagerly looking forward to help transform the industry with my skills over a plethora of the most happening cutting-edge technologies.

The following are some of my primary interests.
  • Mobile Robotics
  • Computer Vision
  • Deep Learning
  • Machine Learning
  • Data Science
  • Genetic Algorithms
  • Reinforcement Learning
  • Data Structures
  • Algorithms

Experience

Professional experience0 - 5 years

Languages

EnglishFluent

Project history

Sr. R&D Engineer, Team Lead, Core Developer

Autonomous Logistics Technologies

Internet & IT

10-50 team member

6) Alog T-1000

Organization: Autonomous Logistics Technologies

Duration: July 2022 - September 2022

Roles: Team Lead, Core Developer

Description: An Autonomous Mobile Robot named "T-1000" is designed, developed
and tested targeting the maximum payloads of 1000kg in order to optimize the logistics
of a renowned manufacturing company in India. ROS Navigation stack is used. Alog
Shuttle application is integrated to facilitate user interface.


R&D Engineer, Team Lead, Core Developer

Autonomous Logistics Technologies
5) A Pilot Project for a E-commerce Organization

Organization: Autonomous Logistics Technologies

Duration: August 2021 - October 2021

Roles: Team Lead, Core Developer

Description: Developing and Testing an autonomously navigating robot in the
warehouse of a well renowned e-commerce organization is the objective of this pilot.
ROS Navigation stack is deployed. Alog Shuttle web application along with the
integration of Alog AWACS(a fleet management tool) is accomplished. The robot is
designed and developed targeting the autonomous navigation in the warehouses for the
optimization of logistics.

R&D Engineer, Team Lead, Core Developer

Autonomous Logistics Technologies
3) ALOG SHUTTLE (Fleet management application for ACDC)

Organization: Autonomous Logistics Technologies

Duration: Dec 2020 - Mar 2021

Roles: Team Lead, Core Developer

Description: An application that manages and tracks a fleet of robots in a warehouse in
shuttle mode. Shuttle mode of navigation involves having predefined routes with in a
warehouse and each robot can be assigned with some of the available routes. A web
application running on each robot's nuc acts as a server and can be accessed from any
mobile to pair up with. A human associate will be operating the app from his mobile to
control the robot traveling in it's specified routes.




The control actions a human associate can take on a robot are:

Select a route.
Select the next station in the route.
Pause/Resume the navigation.
Force stop the robot in an emergency.
My Contributions: I alone developed the entire stack of the application.

R&D Engineer, Team Lead, Core Developer

Autonomous Logistics Technologies
1) ALOG CCS (Visual inspection system using smart surveillance)

Organization: Autonomous Logistics Technologies

Duration: Jan 2020 - Aug 2020

Roles: Team Lead, Core Developer

Description: With the aid of deep-learning models and computer-vision techniques, the
application makes a surveillance camera smart that can monitor and track any possible
fraudulent events with in the field-of-view.

Key features:

Novelty in designing the model that outperforms any standard object detectors such as
YOLO, SSD, etc in this specific use case in terms of both accuracy and inference
time.
Machine specific optimizations on the model to decrease the inference time by 3
times.
Can be deployed on CPU.
Accuracy > 99%.

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