Reference : Model Predictive Control for Aerial Collision Avoidance in Dynamic Environments

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/37031

Title :	Model Predictive Control for Aerial Collision Avoidance in Dynamic Environments
Language :	English
Author, co-author :	Castillo Lopez, Manuel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Sajadi Alamdari, Seyed Amin [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Sanchez Lopez, Jose Luis [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Olivares Mendez, Miguel Angel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Voos, Holger [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit > ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)]
Publication date :	Jun-2018
Main document title :	26th Mediterranean Conference on Control and Automation (MED), Zadar, Croatia, 19-22 June 2018
Peer reviewed :	Yes
Event name :	26th Mediterranean Conference on Control and Automation (MED)
Event date :	19-22 June 2018
Keywords :	[en] Model Predictive Control ; Aerial Robot ; Obstacle Avoidance ; Optimal Control ; UAV ; Autonomous Navigation
Abstract :	[en] Autonomous navigation in unknown environments populated by humans and other robots is one of the main challenges when working with mobile robots. In this paper, we present a new approach to dynamic collision avoidance for multi-rotor unmanned aerial vehicles (UAVs). A new nonlinear model predictive control (NMPC) approach is proposed to safely navigate in a workspace populated by static and/or moving obstacles. The uniqueness of our approach lies in its ability to anticipate the dynamics of multiple obstacles, avoiding them in real-time. Exploiting active set algorithms, only the obstacles that affect to the UAV during the prediction horizon are considered at each sample time. We also improve the fluency of avoidance maneuvers by reformulating the obstacles as orientable ellipsoids, being less prone to local minima and allowing the definition of a preferred avoidance direction. Finally, we present two real-time implementations based on simulation. The former demonstrates that our approach outperforms its analog static formulation in terms of safety and efficiency. The latter shows its capability to avoid multiple dynamic obstacles.
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Automation & Robotics Research Group
Funders :	Fonds National de la Recherche - FnR
Target :	Researchers ; Professionals
Permalink :	http://hdl.handle.net/10993/37031
DOI :	10.1109/MED.2018.8442967
FnR project :	FnR ; FNR10484117 > Holger Voos > BEST-RPAS > Robust Emergency Sense-and-Avoid Capability for Small Remotely Piloted Aerial Systems > 01/02/2016 > 31/01/2019 > 2015

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