AUTONOMOUS LUGGAGE HANDLING SYSTEM

Abstract

Problems with swift, safe, and effective baggage management have arisen in response to the rising number of passengers using railroads. Conventional methods of baggage handling are notoriously slow, error-prone, and labor-intensive, which in turn causes delays and unhappy customers. An Autonomous Luggage Handling System (ALHS) for trains is proposed in this research to overcome these difficulties. The ALHS automates the whole baggage handling process, from check-in to delivery at the destination, by using sophisticated robotics, artificial intelligence, and Internet of Things (IoT) technology. Robotic arms, conveyor belts, and smart baggage carts all work together to make quick work of transporting bags. To guarantee precision and safety, machine learning algorithms are used for the real-time identifying, sorting, and tracking of bags. Internet of Things (IoT) sensors also monitor the state of passengers' bags, sending updates in real time to a command center that can then handle the situation more efficiently and identify problems before they escalate. Reduced manual labor, increased operational efficiency, and better wait times and baggage handling for passengers are all goals of the proposed system. The ALHS shows great promise in automating, enhancing, and improving train baggage management via simulation and prototype testing, which might lead to more dependable, user-friendly, and automated transportation services.