Robust collision avoidance for multiple micro aerial vehicles using nonlinear model predictive control

Robust collision avoidance for multiple micro aerial vehicles using nonlinear model predictive control

Kamel, Mina (ETH Zürich)
Alonso-Mora, J. (TU Delft Learning & Autonomous Control)
Siegwart, Roland (ETH Zürich)
Nieto, Juan (ETH Zürich)

Bicchi, A (editor)
Maciejewski, T (editor)

2017

When several Multirotor Micro Aerial Vehicles (MAVs) share the same airspace, reliable and robust collision avoidance is required. In this paper we address the problem of multi-MAV reactive collision avoidance. We employ a model-based controller to simultaneously track a reference trajectory and avoid collisions. Moreover, to achieve a higher degree of robustness, our method also accounts for the uncertainty of the state estimator and of the position and velocity of the other agents. The proposed approach is decentralized, does not require a collision-free reference trajectory and accounts for the full MAV dynamics. We validated our approach in simulation and experimentally with two MAV.

Collision avoidance
Trajectory
Cost function
Trajectory tracking
Robustness
Vehicle dynamics

http://resolver.tudelft.nl/uuid:1d6d2497-a76b-42e0-8b15-88a1df89a0a4

https://doi.org/10.1109/IROS.2017.8202163

IEEE, Piscataway, NJ, USA

978-1-5386-2682-5

Proceedings 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

IROS 2017: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017-09-24 → 2017-09-28, Vancouver, Canada

Accepted Author Manuscript

Institutional Repository

conference paper

© 2017 Mina Kamel, J. Alonso-Mora, Roland Siegwart, Juan Nieto