II B.Tech._Mini-Project_A3_Poster_Presentation_Template-EE299[1].pptx

II B.Tech._Mini-Project_A3_Poster_Presentation_Template-EE299[1].pptx

1. Smart Driving Delivery Robot II B. Tech. II Sem. (AY: 2024-25) Poster Presentation EE-299 (Mini Project - I) Date: 27-03-2025(03:00 P.M. to 05:00 P.M.) Venue: SRK Complex Presentation By: 1. Tejendra Vijayvargiya (523180 ) 2. Anuj Rajpoot (523106) 3. M. S udhamshu (523142 ) 4. Praganaya Raj (523163 ) Mentor: Dr. Jami Rajesh Abstract: With the increasing demand for contactless delivery solutions, autonomous delivery robots are emerging as a promising technology. This project focuses on designing a self-driving delivery robot using Raspberry Pi 4, Mega Arduino, GPS, LiDAR, motor drivers and other essential components. The robot is capable of navigating autonomously, avoiding obstacles, and delivering packages efficiently. The system integrates real-time navigation, obstacle detection, making it ideal for smart cities and last-mile delivery solutions. This technology has practical applications in e-commerce, food delivery, and healthcare . The results will be evaluated based on delivery time, obstacle avoidance accuracy, and localization precision. Introduction: Autonomous delivery robots are transforming logistics by reducing human dependency and increasing efficiency in package delivery. These robots operate on predefined routes or can dynamically navigate using GPS and obstacle detection. By utilizing a combination of controllers, and our system ensures safe, accurate, and real-time deliveries. Key Features: Autonomous navigation using GPS and LiDAR. Real-time obstacle detection for collision avoidance. Efficient motor control using Mega Arduino and motor driver. Raspberry pi 4 is working as a central processing unit. Raspberry Pi 4 processes sensor data and calculates the optimal path. Arduino microcontroller receives movement commands from Raspberry Pi. The GPS system guides the robot toward the destination. LiDAR sensor detects objects, preventing collisions. If an obstacle is detected, the robot recalculates the path dynamically. Conclusion: This project demonstrates a fully autonomous delivery robot that integrates real-time navigation, obstacle detection, and wireless communication for efficient last-mile delivery. The system provides a cost-effective, energy-efficient, and scalable solution for future delivery networks. Block diagram: Proposed Methodology : The development of an Autonomous Delivery Robot involves both hardware and software integration to achieve efficient navigation, obstacle avoidance, and remote monitoring. 1. Hardware Design: The robot's hardware consists of a chassis, motors, sensors, and a power system. The frame is lightweight and durable, allowing it to move smoothly on different terrains. 2. Data collection: GPS module fetches the robot’s real-time location. LiDAR sensor scans the environment for obstacles. 3. Software Development: The robot's intelligence is powered by a microcontroller (Arduino) for motor control and a processing unit (Raspberry Pi or Jetson Nano) for AI computations. We are using voltage convertor to convert the 11.8v to 5.1v . The robot moves to the target location autonomously, upon reaching the destination the ensures accurate package drop-off. Environment-friendly operation with optimized energy usage. Problem Statement: Traditional delivery systems rely on manual labor, leading to delays, high costs, and environmental impact due to fuel consumption. This project addresses these challenges by implementing a cost-effective and efficient autonomous delivery robot that: Reduces human intervention in last-mile delivery. Minimizes operational costs with an electric power system. Ensures safe and obstacle-free navigation using LiDAR and GPS. Provides real-time route optimization for efficiency. Objectives: The primary objective is to design and develop an autonomous delivery robot that: 1. Uses GPS to determine its current location and target destination. 2. Processes navigation commands using Raspberry Pi 4. 3. Avoids obstacles using LiDAR sensors. 4. Controls movement efficiently via Arduino and Motor Driver. 5. Optimizes route planning for faster and efficient delivery. 6. Ensures reliable delivery performance with precise path planning.