ANIL BABU BOLLINA
Spring Hill,TN
Summary
Results-driven engineer with over 8 years of experience building and deploying production-grade vision systems for industrial automation. Specializes in multi-camera calibration (intrinsic, extrinsic, and hand-eye), depth/TOF/laser line/IR sensing, and 2D/3D perception for object detection, instance segmentation, classification, and inspection. Designs end-to-end solutions from data acquisition and labeling through model development and optimization, all the way to deployment on robots and PLC-controlled cells, with live monitoring, analytics, and continuous improvement. Experienced in both greenfield installs and retrofits across pallet sorting, inline pallet inspection, depalletizing, mixed-SKU palletization, bag/box picking, and order fulfillment. Collaborates closely with controls, mechanical, and software teams to deliver robust, maintainable automation in demanding warehouse, and manufacturing environments.
Overview
8
8
years of professional experience
Work History
Senior Computer Vision Engineer
Universal Robotics Inc
Nashville, TN
04.2021 - Current
- Lead the architecture and deployment of computer-vision–guided automation cells including inline pallet inspection, pallet sorting, depalletizing, mixed-SKU palletization, and bag/box picking for high-mix, high-volume supply chain operations.
- Design and implement perception models using Mask R-CNN, YOLO, Detectron2, and classical computer vision, covering object detection, instance segmentation, classification, and 2D/3D measurement for cartons, bags, pallets, and irregular goods.
- Own multi-camera calibration workflows (intrinsic/extrinsic calibration, robot hand–eye, RGB-D sensor alignment) for depth, time-of-flight, laser-line, and IR cameras, ensuring millimeter-level accuracy over large work envelopes.
- Develop 3D and point cloud algorithms (RGB-D fusion, 3D bounding boxes, surface analysis) that support pallet grading, defect detection, and pick-point generation under variable lighting and clutter.
- Contribute to the Neocortex pallet inspection system, using high-resolution 3D sensors above and below the pallet to detect cracks, broken boards, missing components, protruding nails, and other defects, returning pass/fail decisions in near real time.
- Contribute to the Neocortex pallet sorter, a turn-key robotic pallet sorting cell capable of handling a wide range of pallet types, slip sheets, and debris at high throughput; design the vision layer that grades pallets and routes them to the correct lane while capturing rich analytics.
- Support mixed-SKU palletizing and depalletizing applications by building robust object detection and pose-estimation pipelines that achieve stable picks across cartons, bags, parts, and finished goods with varying stack heights and packaging.
- Integrate vision software with PLCs, robot controllers, and conveyor I/O, implementing deterministic signaling, error handling, safety interlocks, and recovery sequences to meet cycle-time and reliability requirements.
- Build and maintain data pipelines for training and inference, including dataset curation, labeling standards, experiment tracking, and versioned model deployment using Python, PyTorch, TensorFlow, OpenCV, and PCL.
- Deploy containerized services using Docker and implement monitoring with Prometheus and Grafana to track uptime, cycle time, throughput, stoppage categories, and utilization; turn operational data into actionable recommendations that improve cell runtime and reduce unplanned downtime.
- Partner with cross-functional stakeholders (sales, solutions engineering, customer operations) to translate customer requirements into technical specifications, timelines, and deliverables, and mentor junior engineers on best practices in vision system design.
Computer Vision Engineer
Universal Robotics Inc
Nashville, TN
10.2018 - 04.2021
- Established an end-to-end machine learning and computer vision pipeline for warehouse automation lines, from data capture on the floor through RDBMS storage, model training, and deployment to production systems.
- Developed and tuned object detection and classification models in Python using TensorFlow, PyTorch, and scikit-learn, applying feature engineering, data augmentation, transfer learning, and iterative error analysis to improve accuracy and robustness.
- Implemented camera and sensor evaluation frameworks, comparing depth, color, TOF, and laser-line devices; wrote calibration scripts to standardize setup and maintenance across robotic cells.
- Integrated computer vision modules into existing robotic and PLC-controlled cells, refactoring legacy logic and adding perception to enable more flexible, autonomous operations.
- Containerized inference services with Docker and automated build/test/deploy pipelines using Jenkins CI, reducing deployment time and regression risk for new models and features.
- Collaborated closely with controls and mechanical engineers to debug production issues, improve lighting and optics, and refine conveyor and robot motion profiles based on perception performance.
Software Developer
Cellink
Blacksburg, VA
06.2018 - 10.2018
- Developed 100+ Python and batch scripts for automated feature and regression testing, significantly accelerating QA cycles and reducing manual test effort.
- Built Python ETL utilities to extract, transform, and load data between database tables, ensuring consistency and improving accessibility for analytics, reporting, and engineering investigations.
- Worked with cross-functional teams to identify repetitive tasks that could be automated, improving productivity and standardizing workflows across the software group.
Education
Master of Computer Science -
University of Houston–Clear Lake
05.2018
Bachelor of Science - Electronics & Communication Engineering
Vellore Institute of Technology
05.2016
Skills
- Python and C programming
- Deep learning frameworks
- Model optimization techniques
- Computer vision applications
- Data processing and analysis
- 2D/3D object detection
- Image preprocessing methods
- Camera calibration techniques
- Multi-camera integration
- Robotics and automation systems
- Industrial optics and lighting
- Material handling solutions
- Automated reporting tools
- Continuous integration practices
- Version control systems
Selected Projects Impact
- AI-Driven Pallet Inspection System, Helped design and implement the vision and AI components of a minimally invasive pallet inspection system that uses multi-camera high-resolution 3D sensing to scan pallets from top and bottom, classify them as pass/fail, and detect damage such as broken boards, missing components, protruding nails, and cracks. Integrated the system into existing pallet-handling lines with minimal footprint, enabling automated quality checkpoints for both new and recycled pallets.
- Robotic Pallet Sorting Cell, Contributed to a turn-key pallet sorter powered by Neocortex that handles a wide range of pallet types, slip sheets, and debris. Designed the perception stack that grades pallets based on quality and type, supports throughputs up to hundreds of pallets per hour, and feeds rich analytics back to customers for inventory control and continuous improvement.
- Depalletizing and Mixed-SKU Palletization, Developed computer vision and deep learning solutions for high-speed depalletizing and mixed-SKU palletization cells that perform mixed-SKU pick-and-place across cartons, bags, parts, and finished goods. Implemented robust object detection, pose estimation, and collision-aware pick-point generation that support high pick rates and flexible stacking patterns in dynamic logistics environments.
- Bag/Box Picking and Order Fulfillment Cells, Designed vision-guided picking solutions for order-fulfillment applications, combining 2D and 3D sensing with deep learning-based SKU identification to drive robotic picking of bags and boxes. Optimized perception and grasp planning to reduce mis-picks and improve overall line throughput.
- Operational Diagnostics and Analytics, Built a metrics and monitoring stack using Prometheus, Docker, Grafana, and Python to capture and visualize cell usage, uptime, stoppage types and durations, and throughput. Translated raw operational data into clear reports and action items, helping customers and internal teams identify bottlenecks, schedule maintenance, and systematically improve cell utilization over time.
Technologies Domains
- Computer vision
- Deep learning
- Machine learning
- 2D/3D imaging
- Camera calibration
- Point clouds
- RGB-D sensing
- Time-of-flight cameras
- Laser line scanners
- Industrial automation
- Robotics integration
- PLCs and conveyor systems
- Pallet inspection and grading
- Pallet sorting
- Depalletizing
- Mixed-SKU palletization
- Bag and box picking
- Real-time systems
- Reliability and uptime analytics
- Data pipelines
- MLOps practices
Timeline
Senior Computer Vision Engineer
Universal Robotics Inc
04.2021 - Current
Computer Vision Engineer
Universal Robotics Inc
10.2018 - 04.2021
Software Developer
Cellink
06.2018 - 10.2018
Master of Computer Science -
University of Houston–Clear Lake
Bachelor of Science - Electronics & Communication Engineering
Vellore Institute of Technology