Problem Statement
The last-mile delivery process in the current logistics landscape is often plagued by inefficiencies, delays, and high operational costs. Traditional delivery methods struggle to meet the increasing demand for faster and more reliable deliveries, especially in urban areas. There is a need for innovative solutions to optimize the last-mile delivery process.
Abstract
Design and deploy autonomous delivery systems leveraging robotics and AI to revolutionize the last-mile delivery process. The project aims to develop intelligent and autonomous delivery vehicles capable of navigating diverse environments, avoiding obstacles, and delivering packages efficiently. Integrating advanced AI algorithms, the system will optimize route planning and enhance overall logistics efficiency, addressing challenges associated with traditional last-mile delivery methods
Outcome
The project’s outcome is the successful implementation of autonomous delivery systems that significantly improve the last-mile delivery process. These systems will reduce operational costs, increase delivery speed, and enhance the reliability of package deliveries. Businesses adopting this technology will benefit from improved customer satisfaction, reduced environmental impact, and a more competitive edge in the rapidly evolving logistics and e-commerce sectors. The deployment of autonomous delivery systems will mark a transformative shift in the efficiency and effectiveness of the last-mile delivery process.
Reference
In this paper, we propose to explain the application of automated ground vehicles as delivery robots. This system is developed and designed with the intended application being delivery of items within large campuses and buildings. The system proposed in this paper uses Google maps and direction API for getting the navigation data, but the same can be implemented without using third party application if the map of the intended area of usage is available. Donkey and carrot algorithm has been developed and used for navigation using the data obtained from the used API. To get the data on to the unmanned ground vehicle, a server is set up on the onboard microcontroller which gets the data from the cloud, on which further processing is done.
1.Google Cloud. (2019). Cloud Computing Services | Google Cloud, [online] Available: https://c1oud.google.com/.
2.Firebase. (2019). Firebase Realtime Database | Firebase Realtime Database | Firebase, [online] Available: https://firebase.google.com/docs/database.
3.J. Clerk Maxwell, A Treatise on Electricity and Magnetism, Clarendon:Oxford, vol. 2, pp. 68-73, 1892.
4.M. Kok and T. B. Schon, Magnetometer Calibration Using Inertial Sensors. IEEE Sensors Journal 2009, 2016.
5.Y. Wu and W. Shi, On Calibration of Three-Axis Magnetometer. IEEE Sensors Journal, 2015.