Multi-robot path planning for a swarm of robots that can both fly and drive
Name
Flyingdrones.pdf
Description
Accepted version
Size
5.4 MB
Format
Unknown
Checksum (MD5)
d01840571323ed5a39290075ce04ae53
Author(s)
Araki, Brandon
Strang, John
Pohorecky, Sarah
Qiu, Celine
Naegeli, Tobias
Rus, Daniela L
Date Issued
2017
Journal
Proceedings - IEEE International Conference on Robotics and Automation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Version
Author's final manuscript
Abstract
© 2017 IEEE. The multi-robot path planning problem has been extensively studied for the cases of flying and driving vehicles. However, path planning for the case of vehicles that can both fly and drive has not yet been considered. Driving robots, while stable and energy efficient, are limited to mostly flat terrain. Quadcopters, on the other hand, are agile and highly mobile but have low energy efficiency and limited battery life. Combining a quadcopter with a driving mechanism presents a path planning challenge by enabling the selection of paths based off of both time and energy consumption. In this paper, we introduce a framework for multi-robot path planning for a swarm of flying-and-driving vehicles. By putting a lightweight driving platform on a quadcopter, we create a robust vehicle with an energy efficient driving mode and an agile flight mode. We extend two algorithms, priority planning with Safe Interval Path Planning and a multi-commodity network flow ILP, to accommodate multimodal locomotion, and we show that these algorithms can indeed plan collision-free paths for flying-and-driving vehicles on 3D graphs. Finally, we demonstrate that our system is able to plan paths and control the motions of 8 of our vehicles in a miniature town.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICRA.2017.7989657
