Mingda Ma, Alvin Sun, Jialiang Zhang
# Team Members
* Yixiao Sun (yixiaos3)
* Mingda Ma (mingdam2)
* Jialiang Zhang (jz23)
# Problem Statement
Autonomous delivery over drone networks has become one of the new trends which can save a tremendous amount of labor. However, it is very difficult to scale things up due to the inefficiency of multi-rotors collaboration especially when they are carrying payload. In order to actually have it deployed in big cities, we could take advantage of the large ground vehicle network which already exists with rideshare companies like Uber and Lyft. The roof of an automobile has plenty of spaces to hold regular size packages with magnets, and the drone network can then optimize for flight time and efficiency while factoring in ground vehicle plans. While dramatically increasing delivery coverage and efficiency, such strategy raises a challenging problem of drone docking onto moving ground vehicles.
We aim at tackling a particular component of this project given the scope and time limitation. We will implement a decentralized multi-agent control system that involves synchronizing a ground vehicle and a drone when in close proximity. Assumptions such as knowledge of vehicle states will be made, as this project is aiming towards a proof of concepts of a core challenge to this project. However, as we progress, we aim at lifting as many of those assumptions as possible. The infrastructure of the lab, drone and ground vehicle will be provided by our kind sponsor Professor Naira Hovakimyan. When the drone approaches the target and starts to have visuals on the ground vehicle, it will automatically send a docking request through an RF module. The RF receiver on the vehicle will then automatically turn on its assistant devices such as specific LED light patterns which aids motion synchronization between ground and areo vehicles. The ground vehicle will also periodically send out locally planned paths to the drone for it to predict the ground vehicle’s trajectory a couple of seconds into the future. This prediction can help the drone to stay within close proximity to the ground vehicle by optimizing with a reference trajectory.
### The hardware components include:
Provided by Research Platforms
* A drone
* A ground vehicle
* A camera
Developed by our team
* An LED based docking indicator
* RF communication modules (xbee)
* Onboard compute and communication microprocessor (STM32F4)
* Standalone power source for RF module and processor
# Required Circuit Design
We will integrate the power source, RF communication module and the LED tracking assistant together with our microcontroller within our PCB. The circuit will also automatically trigger the tracking assistant to facilitate its further operations. This special circuit is designed particularly to demonstrate the ability for the drone to precisely track and dock onto the ground vehicle.
# Criterion for Success -- Stages
1. When the ground vehicle is moving slowly in a straight line, the drone can autonomously take off from an arbitrary location and end up following it within close proximity.
2. Drones remains in close proximity when the ground vehicle is slowly turning (or navigating arbitrarily in slow speed)
3. Drone can dock autonomously onto the ground vehicle that is moving slowly in straight line
4. Drone can dock autonomously onto the ground vehicle that is slowly turning
5. Increase the speed of the ground vehicle and successfully perform tracking and / or docking
6. Drone can pick up packages while flying synchronously to the ground vehicle
We consider project completion on stage 3. The stages after that are considered advanced features depending on actual progress.