TY - GEN
T1 - Sampling-based receding horizon collision-free control for a class of Micro Aerial Vehicles
AU - Alexis, Kostas
AU - Papachristos, Christos
AU - Siegwart, Roland
AU - Tzes, Anthony
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/14
Y1 - 2015/7/14
N2 - A novel sampling-based receding horizon control strategy that guarantees collision-free navigation for a class of aerial robots is the topic of this paper. The proposed approach combines the concepts of receding horizon control and sampling-based navigation strategies in order to derive a model-based control framework, which respects input and state constraints, and achieves avoidance of any known obstacle while remaining computationally lightweight even for systems of high-order and complex, convex or non-convex obstacles and long prediction horizons. The control law is applied for the case of a multirotor Micro Aerial Vehicle that optionally also employs its capacity to direct its thrust via a rotors' tilting mechanism. Extensive simulation studies indicate that high performance collision-free navigation is achieved and reasonably long prediction horizons can be handled while remaining applicable for on-board deployment.
AB - A novel sampling-based receding horizon control strategy that guarantees collision-free navigation for a class of aerial robots is the topic of this paper. The proposed approach combines the concepts of receding horizon control and sampling-based navigation strategies in order to derive a model-based control framework, which respects input and state constraints, and achieves avoidance of any known obstacle while remaining computationally lightweight even for systems of high-order and complex, convex or non-convex obstacles and long prediction horizons. The control law is applied for the case of a multirotor Micro Aerial Vehicle that optionally also employs its capacity to direct its thrust via a rotors' tilting mechanism. Extensive simulation studies indicate that high performance collision-free navigation is achieved and reasonably long prediction horizons can be handled while remaining applicable for on-board deployment.
KW - Aerial Robots
KW - Receding Horizon Control
KW - Sampling-based methods
KW - Unmanned Aerial Vehicles
UR - http://www.scopus.com/inward/record.url?scp=84945964545&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84945964545&partnerID=8YFLogxK
U2 - 10.1109/MED.2015.7158824
DO - 10.1109/MED.2015.7158824
M3 - Conference contribution
AN - SCOPUS:84945964545
T3 - 2015 23rd Mediterranean Conference on Control and Automation, MED 2015 - Conference Proceedings
SP - 675
EP - 680
BT - 2015 23rd Mediterranean Conference on Control and Automation, MED 2015 - Conference Proceedings
A2 - Munoz, Victor
A2 - Quevedo, Joseba
A2 - Martinez, Jorge L.
A2 - Morales, Jesus
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd Mediterranean Conference on Control and Automation, MED 2015
Y2 - 16 June 2015 through 19 June 2015
ER -