Overview
In order for fleets of drones managed by different operators to share an airspace, they require a fast, reliable and fair Collision Avoidance (CA) approach to ensure safety. This project aims to develop such methods by combining learning-based decision making and decentralized Model Predictive Control to perform on-the-fly, predictive Collision Avoidance (CA) between two (co-operative) drones. In addition to performing CA in real-time, L2F also ensures that the drones do not violate their original mission requirements. The video below shows a simulation where two pairs of drones are have conflicting pre-planned trajectories, and also shows the resulting safe trajectories obtained via L2F.
Publications
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Mission-aware, Learning-to-Fly: Learning-based Collision Avoidance for Scalable Urban Air Mobility
Rodionova, Pant, Jang, Abbas, Mangharam
IEEE International Conference on Intelligent Transportation Systems (ITSC), 2020. -
Learning-to-Fly RL: Reinforcement Learning-based Collision Avoidance for Scalable Urban Air Mobility
Jang, Pant, Rodionova, Mangharam
AIAA/IEEE Digital Avionics Systems Conference (DASC), 2020.
Contributors
Rodionova, Pant, Jang, Abbas, Mangharam
Citation
