The utilisation of the TC200 Autonomous Mobile Robot (AMR) brings autonomous navigation to the forefront. This enables the robot to autonomously traverse the warehouse, deftly avoiding obstacles while optimising path planning. By automating navigation, this project eliminates the need for manual laborious tasks and reduces the risk of human errors, significantly enhancing warehouse productivity. Object tracking, the project's second key component, leverages advanced computer vision algorithms and sensors like a camera and LIDAR to precisely detect and monitor objects, particularly coloured storage bins. This innovation streamlines the retrieval process, making it faster and more efficient. It also minimises errors in object identification, contributing to the project's overall accuracy.

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In the e-Yantra Sahayak Bot theme, the goal was to alleviate lab manpower by deploying an AGV that can autonomously traverse indoor layouts and shuttle boxes between pick-up and drop-off zones. I implemented ROS1 packages for environment mapping, path planning, obstacle avoidance, and object handling, then integrated them into a cohesive navigation pipeline.

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Built a ROS1 pipeline that detects red objects from a live webcam feed. A publisher node captures frames and publishes them as sensor_msgs/Image; a subscriber uses cv_bridge to convert to OpenCV, thresholds in HSV for red, extracts contours, and draws overlays around detected objects. This provides a simple, reliable front-end for tasks like tracking or triggering robot actions.

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