Sumit Namdeo Gore

Developed and implemented ROS-based LIDAR SLAM using C++ for multi-kilometer point cloud mapping, achieving high-precision localization for diverse autonomous vehicles in the Belntelli project.

Optimized Nav2 navigation planner and motion controller for a Delivery Robot, significantly enhancing overall robot performance and efficiency.

Integrated and debugged critical LiDAR, camera, and INS sensors, resolving complex hardware issues and developing robust C++/Python software to ensure safe and reliable system operation.

Developed an internal knowledge base portal utilizing Astro and React, significantly improving access to autonomous vehicle documentation and fostering knowledge sharing.

Maintained and optimized Kubernetes clusters for the Autonomous Vehicle Stack on OpenStack, ensuring 99% uptime for critical real-time applications.

Engineered C-based UWB localization software (DWM-1001C) leveraging Double Sided Two Way Ranging, improving indoor localization accuracy by 30% for robotic systems.

Integrated ROS-based Machine Learning for object detection, significantly enhancing navigation accuracy and safety protocols for autonomous drones.

Implemented Python-based Quintic Polynomial motion planning and PID control algorithms, ensuring safe and precise navigation for autonomous drones.

Spearheaded the design and mass production of cost-effective embedded systems, leading an R&D team of 4 and directly contributing to a 200% increase in revenue within two years.

Managed the complete product lifecycle for Vending Machines, encompassing market research, roadmap development, launch, and iterative improvements.

Facilitated seamless collaboration across manufacturing, design, sales, and operations teams, streamlining cross-functional workflows and accelerating project delivery.

Proactively identified and mitigated technical, market, and strategic risks, preventing project delays and ensuring 100% on-schedule delivery.

Successfully implemented Agile methodologies, optimizing product development cycles, enhancing team collaboration, and accelerating time-to-market delivery.

Led the development of embedded software in C for vending machines, alongside mass programming hardware and software for ESP and AVR microcontrollers.

Designed and implemented a Jaggery (Raw Sugar) manufacturing process that reduced human intervention by 90%, significantly improving product quality and achieving pharmaceutical-grade production standards.

Managed global machinery selection and procurement, achieving 10-20% savings in proposed capital investment while ensuring strict adherence to quality and hygiene standards.

Engineered and architected robust electrical systems and PLC control systems utilizing CAD tools like AutoCAD for optimized operational control.

Collaborated cross-functionally with mechanical, civil, and electrical engineering teams to streamline EPC planning and project execution.

Conceptualized and developed a real-time Driver Drowsiness Detection System, leveraging Raspberry Pi as the core processing platform.

Integrated camera and accelerometers with the Raspberry Pi platform to capture real-time driver data.

Implemented OpenCV and a custom algorithm for acceleration pattern detection, forming the basis of the Driver Drowsiness Detection System.