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Parvathi Primilla
Entry-level Robotics Engineer specializing in autonomous navigation and computer vision
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Robotics/IoT engineer who deployed a fog-enabled real-time monitoring system (edge Raspberry Pi + MQTT + cloud logging) and validated it via an IEEE-indexed publication. Strong in autonomous navigation with ROS/Gazebo, SLAM/localization, and cross-layer debugging using timing/transform-delay correlation. Extends Python computer vision pipelines (YOLO + OpenCV/Albumentations) for custom datasets and weather-specific conditions.
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Key Strengths
- Deployed fog-enabled IoT system with edge (Raspberry Pi) + cloud logging for low-latency reliability
- MQTT architecture decisions based on message criticality (QoS 0 telemetry vs QoS 1 alerts)
- Data-driven validation of filtering/noise reduction using raw vs filtered stream comparisons and statistical metrics
- Systematic cross-layer debugging in robotics (hardware sensors, ROS stack, SLAM/localization replay, timing correlation)
- Hands-on autonomous navigation, SLAM, sensor fusion, and Gazebo simulation experience
- Customized YOLO pipelines for domain conditions (weather-specific augmentations) without modifying core model
- End-to-end integration of ROS navigation stack (SLAM + localization + move_base) into a working autonomous system
- Methodical real-time debugging using RViz/TF inspection and component-by-component isolation
- Navigation performance tuning (gmapping/AMCL parameters, costmap resolution, inflation radius, controller frequencies) to reduce drift/oscillation
- Simulation-driven validation in Gazebo with iterative test runs to achieve reliable obstacle avoidance
- Distributed robotics/IoT communication experience across ESP32, Raspberry Pi, and cloud via MQTT
Reference Highlights
- Very good technical abilities in robotics software
- Delivered excellent projects
- Strong debugging and issue-solving
- Effective problem-solving under time/feasibility constraints
- Able to optimize robot route selection
- Able to establish priorities and communication between robots
- Communicates very well
- Easy to work with
- Works well in groups
- Engaged
- Self-learning
- Motivated
- Solid knowledge of robotics design and programming
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Experience
Education
Awards
Skills
Languages
Publications
1 publication
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